Production Forecast
Pengantar
Kelapa sawit (Elais Guineensis Jacq) sebagai tanaman penghasil minyak sawit mentah atau Crude Palm Oil (CPO) dan minyak inti sawit (palm kernel oil) merupakan salah satu tanaman perkebunan yang hanya bisa tumbuh di daerah sekitar ekuator bersifat tropis dan basah (lembab, dengan RH – 85%) dengan suhu berkisar 24-32 C sepanjang tahun, sinar matahari melimpah, curah hujan tinggi (~2,000 mm). Hal ini yang menyebabkan wilayah Indonesia sangat cocok ditanami pohon kelapa sawit.
Berdasarkan data Badan Pusat Statistik tahun 2016 - 2018, berikut merupakan wilayah perkebunan kelapa sawit di Indonesia:
Produksi minyak sawit dunia didominasi oleh Indonesia dan Malaysia. Kedua negara ini secara total menghasilkan sekitar 85-90% dari total produksi minyak sawit dunia. Indonesia adalah produsen dan eksportir minyak sawit yang terbesar. Mayoritas hasil produksi minyak kelapa sawit Indonesia diekspor dan ngara-negara tujuan ekspor yang paling penting adalah RRT, India, Pakistan, Malaysia, dan Belanda.
Tabel di atas menunjukkan bahwa produksi kelapa sawit naik drastis selama satu dekade terakhir. Gabungan Pengusaha Kelapa Sawit Indonesia (Gapki) menyatakan Indonesia bisa memproduksi paling tidak 40 juta ton kelapa sawit per tahun mulai dari tahun 2020.
Industri perkebunan dan pengolahan sawit adalah industri kunci bagi perekonomian Indonesia: ekspor minyak kelapa sawit adalah penghasil devisa yang penting dan industri ini memberikan kesempatan kerja bagi jutaan orang Indonesia. Dalam hal pertanian, minyak sawit merupakan industri terpenting di Indonesia yang menyumbang di antara 1,5 - 2,5 persen terhadap total produk domestik bruto (PDB).
Tujuan
Produksi kelapa sawit sangat dipengaruhi oleh aktivitas perawatan dan pemupukan yang dapat mempengaruhi baik dari sisi kualitas maupun kuantitas buah kelapa sawit. Proses melakukan forecasting hasil produksi kelapa sawit umum dilakukan karena dari hasil forecasting tsb, tim dapat merencanakan aktivitas - aktivitas terkait, misalkan jumlah pupuk yang perlu diberikan untuk mencapai hasil produksi yang diinginkan atau jumlah pemanen yang harus dipersiapkan agar hasil produksi dapat dipanen dengan optimal.
Pada project ini, saya akan melakukan forecasting produksi kelapa sawit di sebuah perkebunan PT XYZ. Data yang diperoleh merupakan data produksi kelapa sawit perusahaan tsb sejak tahun 2013. Berikut contoh datanya :
Penjelasan untuk masing-masing atribut data di atas adalah :
GROUP_AREA: grup dari area perkebunanSUB_GROUP_AREA: sub grup dari area perkebunanAREA: area perkebunanPERIOD: periode data dalam bulananJANJANG_PRODUKSI: total produksi yang dihasilkan dalam bulan tersebut (dalam satuan janjang)TON_PRODUKSI: total produksi yang dihasilkan dalam bulan tersebut (dalam satuan tonase)AVG_KG_JANJANG: rata-rata berat janjang di bulan tersebut (dalam satuan kilogram)JANJANG_PER_POKOK: rata-rata jumlah janjang yang dihasilkan per pokok panen di bulan tersebut (dalam satuan janjang)STATUS: ID status area, terdiri dari : 1. M, 2. SHA_TANAM: total area yang ditanam di sebuah area (dalam satuan hectare)POKOK_TANAM: total pokok kelapa sawit yang ditanam di sebuah areaTIPE_TANAH: tipe tanah, terdiri dari : 1. MINERAL, 2. OCM, 3. SANDY, 4. PEATBIBIT: jenis bibit yang ditanam di suatu area, terdiri dari : 1. SOCFINDO, 2. LONSUM, 3. BINA SAWIT, 4. MARIHAT, 5. TOPAS, 6. COSTARICATIPE_LAHAN: tipe lahan, menandakan tingkat kecocokan lahan tersebut terhadap tanaman kelapa sawit, terdiri dari : 1. S1, 2. S2, 3. S3, 4. NTAHUN_TANAM: menunjukkan tahun kapan area tersebut mulai ditanamUMUR_TANAMAN: menunjukkan usia tanaman sejak mulai ditanam sampai periode tersebut (dalam satuan tahun)
Forecast akan dilakukan dengan 2 cara, yaitu : * melakukan forecast janjang per pokok (dalam satuan janjang) dan rata-rata berat janjang (dalam satuan kilogram) baru setelah itu menghitung nilai produksi * melakukan forecast langsung nilai produksi (dalam satuan tonase)
Eksplorasi Data
Jika ditampilkan dalam bentuk tren bulanan, maka hasil produksi per Grup Area adalah:
Untuk jenis bibit, tipe lahan dan tahun tanam yang sama, perkembangan produksi kelapa sawit memiliki pola yang sama. Kita akan ambil sample data dengan tahun tanam 2006 - 2010, jika kita lihat data kita berdasarkan tipe lahan dan jenis bibit:
Ketika umur kelapa sawit semakin tua, maka buah kelapa sawit yang dihasilkan juga semakin besar. Hal tersebut ditunjukkan dengan rata-rata berat janjang yang akan cenderung meningkat.
Pre-processing Data
Sebelum kita mulai melakukan pembuatan model, tahap yang harus kita lakukan ada pre-processing data. Kita akan membuang variabel-variabel yang tidak akan kita gunakan dalam pembuatan model machine learning kita:
harvest_data <- harvest_data %>%
select(GROUP_AREA, SUB_GROUP_AREA, AREA, BIBIT, TIPE_LAHAN, TAHUN_TANAM, UMUR_TANAMAN, PERIOD,
TON_PRODUKSI, AVG_KG_JANJANG, JANJANG_PER_POKOK, JANJANG_PRODUKSI, POKOK_TANAM)Karena data kita berupa time series maka ada beberapa hal yang harus kita pastikan terlebih dulu:
1. Tidak diperkenankan ada period waktu yang tidak lengkap.
Kita akan cek range data dari variabel PERIOD terlebih dulu:
## [1] "2013-01-01" "2019-12-01"Dari data di atas, diketahui range data kita adalah dari 1 Jan 2013 sampai 11 Nov 2019.
Tahap berikutnya adalah kita memastikan jumlah data per AREA:
Berdasarkan data di atas, diketahui bahwa jumlah data per AREA tidak sama, artinya di data kita ada period waktu yang tidak lengkap. Untuk itu kita akan melengkapi period waktu yang tidak lengkap terlebih dulu:
harvest_data <- harvest_data %>%
pad(interval = 'month',
start_val = min(harvest_data$PERIOD),
end_val = max(harvest_data$PERIOD),
group = c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM"))Setelah dilakukan fungsi pad(), jumlah data per AREA sudah sama yaitu sebanyak 83 data per area.
2. Data harus urut berdasarkan periode waktu
Kita akan mengurutkan data kita terlebih dulu:
3. Tidak diperkenankan ada nilai yang hilang (N/A)
Kita akan membuang data tanaman sebelum tanaman tsb mulai panen terlebih dulu :
Cek apakah di data kita ada nilai yang hilang (N/A):
## GROUP_AREA SUB_GROUP_AREA AREA BIBIT
## 0 0 0 0
## TIPE_LAHAN TAHUN_TANAM UMUR_TANAMAN PERIOD
## 0 0 0 0
## TON_PRODUKSI AVG_KG_JANJANG JANJANG_PER_POKOK JANJANG_PRODUKSI
## 0 0 0 0
## POKOK_TANAM mulai_panen
## 0 0Ternyata tidak ada data yang tidak memiliki nilai.
Cross Validation
Sebelum kita membuat model, kita harus membagi data kita menjadi data train, data test, dan data validation terlebih dulu. Data train akan kita gunakan melakukan training terhadap model kita sedangkan data test & data validation akan digunakan menjadi pembanding dan melihat apakah model kita overfit / underfit untuk melakukan prediksi terhadap data baru di luar data training nya. Kita akan menggunakan 2 tahun terakhir dari data kita untuk menjadi data test & data validation dan sisanya akan kita gunakan menjadi data train :
# range dalam bulan
countData <- length(unique(harvest_data$PERIOD))
# jumlah data validation, data test dan data train
# val_size <- 12
test_size <- 12
train_size <- countData - test_size
# range awal dan range akhir untuk masing2 data
# val_end <- max(harvest_data$PERIOD)
# val_start <- val_end %m-% months(val_size-1)
# test_end <- val_start %m-% months(1)
test_end <- max(harvest_data$PERIOD)
test_start <- test_end %m-% months(test_size-1)
train_end <- test_start %m-% months(1)
train_start <- min(harvest_data$PERIOD)
# interval untuk masing2 data
interval_train <- interval(train_start, train_end)
interval_test <- interval(test_start, test_end)
# interval_val <- interval(val_start, val_end)Pembuatan Model Machine Learning
Forecast Berdasarkan Janjang per Pokok dan Rata-rata Berat Janjang
FORECAST JANJANG PER POKOK
Dikarenakan janjang per pokok baru mulai stabil di usia > 6 tahun, maka kita hanya akan menggunakan forecast untuk yg ditanam tahun 2006 dan sebelumnya :
harvest_data_jp <- harvest_data %>%
select(GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM, PERIOD, JANJANG_PER_POKOK) %>%
mutate(
IDX = paste(GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM, sep = "-"),
value = JANJANG_PER_POKOK
) %>%
ungroup() %>%
select(IDX, PERIOD, value) %>%
spread(IDX, value)
head(harvest_data_jp)## # A tibble: 6 x 66
## PERIOD `AREA0001-A-A09… `AREA0001-A-A10… `AREA0001-A-A11… `AREA0001-A-A12…
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2013-02-01 1.59 1.73 1.81 2.04
## 2 2013-03-01 1.72 3.42 1.89 1.54
## 3 2013-04-01 1.74 1.97 1.66 1.49
## 4 2013-05-01 1.81 1.93 1.23 1.88
## 5 2013-06-01 1.67 1.76 1.09 1.19
## 6 2013-07-01 1.07 0.82 0.86 1.05
## # … with 61 more variables: `AREA0001-A-A13-2006` <dbl>,
## # `AREA0001-A-A14-2006` <dbl>, `AREA0001-A-A15-2006` <dbl>,
## # `AREA0001-A-A16-2006` <dbl>, `AREA0001-A-A17-2006` <dbl>,
## # `AREA0001-A-A18-2006` <dbl>, `AREA0001-A-A19-2006` <dbl>,
## # `AREA0001-A-AA12-2006` <dbl>, `AREA0001-A-B11-2006` <dbl>,
## # `AREA0001-A-B12-2006` <dbl>, `AREA0001-A-B13-2006` <dbl>,
## # `AREA0001-A-B14-2006` <dbl>, `AREA0001-A-B15-2006` <dbl>,
## # `AREA0001-A-B16-2006` <dbl>, `AREA0001-A-B17-2006` <dbl>,
## # `AREA0001-A-B18-2006` <dbl>, `AREA0001-B-C14-2006` <dbl>,
## # `AREA0001-B-C15-2006` <dbl>, `AREA0001-B-C16-2006` <dbl>,
## # `AREA0001-B-C17-2006` <dbl>, `AREA0001-B-C18-2006` <dbl>,
## # `AREA0001-B-D13-2006` <dbl>, `AREA0001-B-D14-2006` <dbl>,
## # `AREA0001-B-D15-2006` <dbl>, `AREA0001-B-D16-2006` <dbl>,
## # `AREA0001-B-D17-2006` <dbl>, `AREA0001-B-D18-2006` <dbl>,
## # `AREA0001-B-D19-2006` <dbl>, `AREA0001-B-E13-2006` <dbl>,
## # `AREA0001-B-E14-2006` <dbl>, `AREA0001-B-E15-2006` <dbl>,
## # `AREA0001-B-E16-2006` <dbl>, `AREA0001-B-E17-2006` <dbl>,
## # `AREA0001-H-I21-2006` <dbl>, `AREA0001-H-I22-2006` <dbl>,
## # `AREA0002-1-E21-2006` <dbl>, `AREA0002-1-E22-2006` <dbl>,
## # `AREA0002-1-E23-2006` <dbl>, `AREA0002-1-E24-2006` <dbl>,
## # `AREA0002-F-A20-2006` <dbl>, `AREA0002-F-A21-2006` <dbl>,
## # `AREA0002-F-A22-2006` <dbl>, `AREA0002-F-A23-2006` <dbl>,
## # `AREA0002-F-A24-2006` <dbl>, `AREA0002-F-A25-2006` <dbl>,
## # `AREA0002-F-A26-2006` <dbl>, `AREA0002-F-B20-2006` <dbl>,
## # `AREA0002-F-B21-2006` <dbl>, `AREA0002-F-B22-2006` <dbl>,
## # `AREA0002-F-B23-2006` <dbl>, `AREA0002-F-B24-2006` <dbl>,
## # `AREA0002-F-B25-2006` <dbl>, `AREA0002-F-B26-2006` <dbl>,
## # `AREA0002-F-C23-2006` <dbl>, `AREA0002-F-C25-2006` <dbl>,
## # `AREA0002-G-G21-2006` <dbl>, `AREA0002-G-G22-2006` <dbl>,
## # `AREA0002-G-H19-2006` <dbl>, `AREA0002-G-H20-2006` <dbl>,
## # `AREA0002-G-H21-2006` <dbl>, `AREA0002-G-H22-2006` <dbl># create recipe
jp_recipe <- recipe(formula = ~.,
data = harvest_data_jp) %>%
#step
step_sqrt(all_numeric()) %>% #melakukan akar
step_center(all_numeric()) %>% #melakukan pengurangan dengan nilai tengah
step_scale(all_numeric()) %>% #melakukan scaling data
#prep
prep()
# revert back function
jp_revert <- function(vector, recipe, varname) {
# store recipe values
recipe_center <- recipe$steps[[2]]$means[varname]
recipe_scale <- recipe$steps[[3]]$sds[varname]
# convert back based on the recipe
results <- (vector * recipe_scale + recipe_center) ^ 2
# add additional adjustment if necessary
results <- round(results, 2)
# return the results
results
}# apply recipe
harvest_data_jp_recipe <- bake(jp_recipe, harvest_data_jp)
# un-pivot data
harvest_data_jp_recipe <- harvest_data_jp_recipe %>%
gather(IDX, value, -PERIOD)
head(harvest_data_jp_recipe)## # A tibble: 6 x 3
## PERIOD IDX value
## <date> <chr> <dbl>
## 1 2013-02-01 AREA0001-A-A09-2006 0.991
## 2 2013-03-01 AREA0001-A-A09-2006 1.15
## 3 2013-04-01 AREA0001-A-A09-2006 1.18
## 4 2013-05-01 AREA0001-A-A09-2006 1.26
## 5 2013-06-01 AREA0001-A-A09-2006 1.09
## 6 2013-07-01 AREA0001-A-A09-2006 0.270harvest_data_jp_recipe <- harvest_data_jp_recipe %>%
mutate(sample = case_when(
PERIOD %within% interval_train ~ "train",
PERIOD %within% interval_test ~ "test"
))
head(harvest_data_jp_recipe)## # A tibble: 6 x 4
## PERIOD IDX value sample
## <date> <chr> <dbl> <chr>
## 1 2013-02-01 AREA0001-A-A09-2006 0.991 train
## 2 2013-03-01 AREA0001-A-A09-2006 1.15 train
## 3 2013-04-01 AREA0001-A-A09-2006 1.18 train
## 4 2013-05-01 AREA0001-A-A09-2006 1.26 train
## 5 2013-06-01 AREA0001-A-A09-2006 1.09 train
## 6 2013-07-01 AREA0001-A-A09-2006 0.270 train# cek tipe time series
check_tipe_ts <- harvest_data_jp_recipe %>%
filter(IDX == "AREA0001-A-A09-2006" & sample == "train") %>%
select(PERIOD, value)
# single seasonal
ts_a <- ts(data = check_tipe_ts$value, frequency = 12)
ts_a %>% decompose() %>% autoplot()
# multi-seasonal
msts_a <- msts(data = check_tipe_ts$value, seasonal.periods = c(12, 30))
msts_a %>% mstl() %>% autoplot()
harvest_data_jp_nest <- harvest_data_jp_recipe %>%
group_by(IDX, sample) %>%
nest(.key = "data") %>%
pivot_wider(names_from = sample, values_from = data)
head(harvest_data_jp_nest)## # A tibble: 6 x 3
## # Groups: IDX [6]
## IDX train test
## <chr> <list<df[,2]>> <list<df[,2]>>
## 1 AREA0001-A-A09-2006 [71 × 2] [12 × 2]
## 2 AREA0001-A-A10-2006 [71 × 2] [12 × 2]
## 3 AREA0001-A-A11-2006 [71 × 2] [12 × 2]
## 4 AREA0001-A-A12-2006 [71 × 2] [12 × 2]
## 5 AREA0001-A-A13-2006 [71 × 2] [12 × 2]
## 6 AREA0001-A-A14-2006 [71 × 2] [12 × 2]# list fungsi yang akan digunakan untuk membentuk time series
define_function <- list(
ts = function(x) ts(x$value, frequency = 12),
msts6 = function(x) msts(x$value, seasonal.periods = c(12, 6)),
msts18 = function(x) msts(x$value, seasonal.periods = c(12, 18)),
msts24 = function(x) msts(x$value, seasonal.periods = c(12, 24)),
msts30 = function(x) msts(x$value, seasonal.periods = c(12, 30))
)
function_list <- define_function %>%
rep(nrow(harvest_data_jp_nest)) %>%
enframe("func_name", "func")
# replicate data harvest_data_jp_nest
harvest_data_jp_nest_rep <- harvest_data_jp_nest[rep(seq_len(nrow(harvest_data_jp_nest)), each = length(define_function)), ]
# kombinasikan function_list dengan masing-masing GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM
harvest_data_jp_nest <- merge(harvest_data_jp_nest_rep, function_list, all.x=TRUE)
harvest_data_jp_nest <- harvest_data_jp_nest %>%
separate(IDX, c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM"), "-") %>%
mutate(
IDX = paste(GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM, sep = "-")
) %>%
select(-c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM")) %>%
distinct_all() %>%
arrange(IDX)
head(harvest_data_jp_nest)## train
## 1 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 2 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 3 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 4 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 5 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 6 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, 0.92739664, 2.62342382, 1.20774090, 1.16225404, 0.96345991, -0.37394993, -0.55500948, -0.46300890, 0.48098942, -0.12195055, 0.01994011, 0.57444184, 0.54797580, 0.15592447, 0.84201316, 0.65276320, 0.41277811, -0.12195055, 0.39898284, 0.05064307, 0.57444184, 0.54797580, 0.49448257, -0.20363316, -0.40923844, 0.03532746, 0.17070015, -0.20363316, 0.80486987, -0.22023395, -0.78980880, -0.23692620, 1.23031114, 2.19568854, -0.37394993, 0.87882230, -0.23692620, -0.65026396, -0.98180442, -0.68935777, -1.66190176, -1.66190176, -1.45329109, 0.28668392, 1.52457793, 2.44024742, -0.02666267, -0.10586868, -1.45329109, -1.82954637, -2.01983155, -1.66190176, -1.48158519, -0.32181449, -0.13811553, 1.11629333, 0.27239482, -0.81039532, -0.78980880, -1.59960561, -2.06153943, -1.00415028, -1.45329109, -0.51783745, -0.61174941, 1.12782874, 1.59795441, 1.54565721, -0.25371144, 0.14108440, 0.27239482
## test
## 1 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 2 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 3 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 4 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 5 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 6 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.20006167, -0.81039532, -0.74914414, -0.25371144, -0.65026396, 0.21464912, 1.37433329, 1.28624546, -0.01105391, 0.44021394, -0.04234740, 0.27239482
## func_name func
## 1 ts function (x) , ts(x$value, frequency = 12)
## 2 msts6 function (x) , msts(x$value, seasonal.periods = c(12, 6))
## 3 msts18 function (x) , msts(x$value, seasonal.periods = c(12, 18))
## 4 msts24 function (x) , msts(x$value, seasonal.periods = c(12, 24))
## 5 msts30 function (x) , msts(x$value, seasonal.periods = c(12, 30))
## 6 ts function (x) , ts(x$value, frequency = 12)
## IDX
## 1 AREA0001-A-A09-2006
## 2 AREA0001-A-A09-2006
## 3 AREA0001-A-A09-2006
## 4 AREA0001-A-A09-2006
## 5 AREA0001-A-A09-2006
## 6 AREA0001-A-A10-2006# list model yang akan digunakan untuk pemodelan time series
define_model <- list(
ets = function(x) ets(x),
auto.arima = function(x) auto.arima(x),
stlm = function(x) stlm(x),
tbats = function(x) tbats(x, use.box.cox = FALSE)
)
model_list <- define_model %>%
rep(nrow(harvest_data_jp_nest)) %>%
enframe("model_name", "model")
# replicate data harvest_data_jp_nest
harvest_data_jp_nest_rep <- harvest_data_jp_nest[rep(seq_len(nrow(harvest_data_jp_nest)), each = length(define_model)), ]
# kombinasikan function_list dengan masing-masing GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM
harvest_data_jp_nest <- merge(harvest_data_jp_nest_rep, model_list, all.x=TRUE)
harvest_data_jp_nest <- harvest_data_jp_nest %>%
filter(
!(model_name == "ets" & stringr::str_detect(func_name,"msts")),
!(model_name == "auto.arima" & stringr::str_detect(func_name,"msts"))
) %>%
distinct_all() %>%
arrange(IDX)
head(harvest_data_jp_nest)## train
## 1 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 2 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 3 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 4 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 5 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 6 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## test
## 1 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 2 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 3 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 4 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 5 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 6 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## func_name func
## 1 ts function (x) , ts(x$value, frequency = 12)
## 2 ts function (x) , ts(x$value, frequency = 12)
## 3 ts function (x) , ts(x$value, frequency = 12)
## 4 msts6 function (x) , msts(x$value, seasonal.periods = c(12, 6))
## 5 msts18 function (x) , msts(x$value, seasonal.periods = c(12, 18))
## 6 msts24 function (x) , msts(x$value, seasonal.periods = c(12, 24))
## IDX model_name model
## 1 AREA0001-A-A09-2006 ets function (x) , ets(x)
## 2 AREA0001-A-A09-2006 auto.arima function (x) , auto.arima(x)
## 3 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 4 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 5 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 6 AREA0001-A-A09-2006 stlm function (x) , stlm(x)harvest_data_jp_nest_res <- harvest_data_jp_nest %>%
mutate(
params = map(train, ~ list(x = .x)),
data = invoke_map(func, params),
params = map(data, ~ list(x = .x)),
fitted = invoke_map(model, params)
) %>%
select(-data, -params)harvest_data_jp_nest_res <- harvest_data_jp_nest_res %>%
mutate(
error = map(fitted, ~ forecast(.x, h = 12)) %>%
map2_dbl(test, ~ rmse_vec(truth = .y$value, estimate = .x$mean))
) %>%
arrange(IDX, error)
harvest_data_jp_nest_res %>%
select(IDX, ends_with("_name"), error) %>%
head()## IDX func_name model_name error
## 1 AREA0001-A-A09-2006 ts auto.arima 0.6480349
## 2 AREA0001-A-A09-2006 msts6 stlm 0.6685432
## 3 AREA0001-A-A09-2006 ts stlm 0.7172550
## 4 AREA0001-A-A09-2006 msts6 tbats 0.7370420
## 5 AREA0001-A-A09-2006 ts tbats 0.7389439
## 6 AREA0001-A-A09-2006 msts24 stlm 0.7455311harvest_data_jp_test <- harvest_data_jp_nest_res %>%
mutate(
forecast = map(fitted, ~ forecast(.x, h = 12)) %>%
map2(test, ~ tibble(PERIOD = .y$PERIOD, value = as.vector(.x$mean))),
key = paste(func_name, model_name, sep = "-")
)
head(harvest_data_jp_test)## train
## 1 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 2 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 3 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 4 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 5 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## 6 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.990697052, 1.151382599, 1.175557095, 1.259092128, 1.090324168, 0.270367565, 0.014505193, 0.405899684, 1.654422006, 0.648262733, 0.031125527, 0.080451777, 0.208304429, -0.339466349, 2.358584996, 0.300964044, 0.522020047, -0.087194984, 0.620587275, -0.104490050, 0.536268407, 0.405899684, -0.002206715, -0.885712184, -1.058618092, -0.555740440, -0.121888372, -0.210496410, 1.151382599, 0.405899684, -0.087194984, 0.270367565, 0.578674894, 2.058803238, -0.597137382, 0.978069052, -0.515027886, -0.747962163, -1.084580364, -1.032995240, -1.397375746, -1.746824965, -1.032995240, 0.129005149, 0.990697052, 1.567066545, 0.435295784, 0.031125527, -1.493778772, -1.828968752, -1.917221642, -1.669673834, -1.746824965, -0.174721889, -0.339466349, 1.444161728, 0.823296920, -0.639254433, 0.270367565, -1.306391693, -2.119343580, -1.493778772, -1.527297245, -0.838828103, -1.248331512, 0.606670320, 1.163487315, 1.151382599, 0.770301554, 0.634451212, 0.285701218
## test
## 1 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 2 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 3 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 4 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 5 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## 6 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.01450519, -0.45517110, -0.43552705, -0.95803742, -0.70386638, -0.63925443, 1.71888167, 0.96540103, -0.01901173, -0.05290741, 0.96540103, 0.08045178
## func_name func
## 1 ts function (x) , ts(x$value, frequency = 12)
## 2 msts6 function (x) , msts(x$value, seasonal.periods = c(12, 6))
## 3 ts function (x) , ts(x$value, frequency = 12)
## 4 msts6 function (x) , msts(x$value, seasonal.periods = c(12, 6))
## 5 ts function (x) , ts(x$value, frequency = 12)
## 6 msts24 function (x) , msts(x$value, seasonal.periods = c(12, 24))
## IDX model_name model
## 1 AREA0001-A-A09-2006 auto.arima function (x) , auto.arima(x)
## 2 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 3 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 4 AREA0001-A-A09-2006 tbats function (x) , tbats(x, use.box.cox = FALSE)
## 5 AREA0001-A-A09-2006 tbats function (x) , tbats(x, use.box.cox = FALSE)
## 6 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## fitted
## 1 1.156096, -0.09625041, -0.4159049, -1.733749, 0.8037716, 0.5910368, 0.01892072, -0.02258876, 0.01146053, -0.01067824, 0.01020309, -0.02258876, 0.03478847, -0.01945617, 0.00857981, -0.008134593, 0.01146053, -0.01945617, 0.01347133, -0.004700014, 0.004015232, -0.01067824, 0.00857981, -0.004700014, 0.01556999, -0.01451005, 0.01020309, -0.008134593, 0.004015232, -0.01451005, 0.0140194, TRUE, TRUE, TRUE, TRUE, TRUE, -79.70526, 171.4105, 3, 2, 0, 0, 12, 1, 0, 0.0009906963, 0.1282598, 0.06013119, 0.05970164, -0.1154826, -0.7236095, -0.3919447, 0.329924, 1.187633, -0.6622026, -1.165069, -0.3275166, -0.07214579, -0.8080374, 2.005857, -1.048932, -0.7844076, -0.4590047, 0.4077129, -0.4344024, 0.2138481, 0.07155344, -0.5431786, -1.155065, -0.811421, -0.0320357, 0.06430612, -0.4762944, 0.8375186, -0.3132354, -0.7527733, 0.3687816, 0.7814303, 2.01105, -1.329757, 0.9947515, -0.160911, -0.538186, -0.3596461, -0.3936294, -0.946665, -1.388091, -0.5414894, 0.3284336, 0.4463126, 0.4987048, -0.7259775, -0.3413758, -0.9352051, -0.4288896, -0.007510609, 0.01480564, -0.4795348, 0.8051244, -0.1053964, 1.263449, 0.2302954, -1.257862, 0.9166106, -0.4271559, -0.9881234, 0.422145, 0.03535847, 0.1713145, -0.6798941, 1.064288, 1.050868, 0.3188887, 0.1662264, 0.5670148, 0.6160458, auto.arima(y = x, x = structure(list(x = structure(c(0.990697052206912, , 1.15138259916338, 1.17555709542418, 1.25909212812449, 1.09032416835215, , 0.270367565274551, 0.0145051932934668, 0.40589968442164, 1.65442200563273, , 0.648262733093922, 0.0311255273763635, 0.0804517771437626, 0.208304429385509, , -0.33946634870886, 2.35858499590098, 0.300964044002608, 0.522020046941882, , -0.0871949836709626, 0.6205872748239, -0.104490049584008, 0.536268406796502, , 0.40589968442164, -0.00220671495726092, -0.885712184438594, -1.05861809188017, , -0.555740439895221, -0.121888372374063, -0.210496409544494, 1.15138259916338, , 0.40589968442164, -0.0871949836709626, 0.270367565274551, 0.578674894193376, , 2.05880323825, 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0.04525101, 0.7270109, -0.2027725, 1.243107, 0.9461002, -0.29316, -0.4600932, -0.3262124, 0.1634506
## 3 0.9906971, 1.151383, 1.175557, 1.259092, 1.090324, 0.2703676, 0.01450519, 0.4058997, 1.654422, 0.6482627, 0.03112553, 0.08045178, 0.2083044, -0.3394663, 2.358585, 0.300964, 0.52202, -0.08719498, 0.6205873, -0.10449, 0.5362684, 0.4058997, -0.002206715, -0.8857122, -1.058618, -0.5557404, -0.1218884, -0.2104964, 1.151383, 0.4058997, -0.08719498, 0.2703676, 0.5786749, 2.058803, -0.5971374, 0.9780691, -0.5150279, -0.7479622, -1.08458, -1.032995, -1.397376, -1.746825, -1.032995, 0.1290051, 0.9906971, 1.567067, 0.4352958, 0.03112553, -1.493779, -1.828969, -1.917222, -1.669674, -1.746825, -0.1747219, -0.3394663, 1.444162, 0.8232969, -0.6392544, 0.2703676, -1.306392, -2.119344, -1.493779, -1.527297, -0.8388281, -1.248332, 0.6066703, 1.163487, 1.151383, 0.7703016, 0.6344512, 0.2857012, 1.386344, 1.284931, 1.183518, 1.082105, 0.999566, 0.9170269, 0.8344879, 0.7601263, 0.6857646, 0.611403, 0.5762442, 0.5410854, 0.5059265, 0.4751423, 0.444358, 0.4135737, 0.3512965, 0.2890193, 0.2267421, 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1.451456, 1.531719, 1.377875, 0.9191163, 0.4771888, 0.223378, 0.4980163, 0.1035948, 0.02383685, 0.08765467, 0.5105135, 0.4101229, 1.506516, 1.06292, 0.8808596, 0.479821, 0.5730325, -0.03670268, -0.2261241, -0.36654, -0.2314682, -0.5484361, -0.4467218, -0.1401623, -0.0376548, 0.08263168, 0.7807807, 0.6779716, 0.2675122, -0.01505458, -0.2024751, 0.5200043, -0.1517478, 0.4760524, 0.3464043, 0.1467767, -0.3684512, -0.4902892, -0.8111516, -1.240647, -1.168577, -0.8081697, -0.3612589, 0.1770483, 0.2164465, 0.1396654, -0.3056314, -0.7139136, -1.165685, -1.181044, -1.286822, -0.6538872, -0.5580843, 0.1332934, -0.01431701, -0.8392389, -0.3656289, -0.8476936, -1.086858, -0.8899496, -1.007046, -0.650716, -0.7502572, 0.00116025, 0.5332848, 0.4599805, 0.1026625, -0.1098016, -0.03089053, 0.5318414, 1.584929, 12, ETS(A,N,N), x, A, N, N, FALSE, ets(y = x, model = etsmodel, allow.multiplicative.trend = allow.multiplicative.trend), 1.584929, 0.5427729, 1.63854, 1.788469, 1.226926, 1.60237, 1.242452, 0.5152897, 0.08817786, -0.00004139305, 0.7397693, -0.2435985, -0.0463709, 0.1438309, 0.8827391, 0.3217531, 2.471628, 0.6724408, 0.7205987, 0.1268029, 0.6550829, -0.5734281, -0.3928642, -0.4901424, -0.1125699, -0.8274502, -0.3571868, 0.1296898, 0.05257839, 0.188515, 1.395333, 0.5874729, -0.0937987, -0.2637868, -0.3674538, 1.155974, -0.7430641, 1.02868, 0.2322804, -0.02894749, -0.8219856, -0.5975383, -1.093594, -1.618714, -1.105136, -0.4909178, 0.0321386, 0.6508986, 0.2511272, 0.07207808, -0.6976083, -1.073308, -1.563361, -1.194564, -1.379935, -0.09673974, -0.4737528, 0.7418854, -0.1442525, -1.565385, 0.05127103, -1.272036, -1.297385, -0.7166189, -1.110122, -0.3370528, -0.8378793, 0.6626028, 1.001693, 0.3954537, -0.2118703, -0.2968251, 0.03857169, function (x, ...) , {, return(ets(x, model = etsmodel, allow.multiplicative.trend = allow.multiplicative.trend, , ...)), }, 0.9906971, 1.151383, 1.175557, 1.259092, 1.090324, 0.2703676, 0.01450519, 0.4058997, 1.654422, 0.6482627, 0.03112553, 0.08045178, 0.2083044, -0.3394663, 2.358585, 0.300964, 0.52202, -0.08719498, 0.6205873, -0.10449, 0.5362684, 0.4058997, -0.002206715, -0.8857122, -1.058618, -0.5557404, -0.1218884, -0.2104964, 1.151383, 0.4058997, -0.08719498, 0.2703676, 0.5786749, 2.058803, -0.5971374, 0.9780691, -0.5150279, -0.7479622, -1.08458, -1.032995, -1.397376, -1.746825, -1.032995, 0.1290051, 0.9906971, 1.567067, 0.4352958, 0.03112553, -1.493779, -1.828969, -1.917222, -1.669674, -1.746825, -0.1747219, -0.3394663, 1.444162, 0.8232969, -0.6392544, 0.2703676, -1.306392, -2.119344, -1.493779, -1.527297, -0.8388281, -1.248332, 0.6066703, 1.163487, 1.151383, 0.7703016, 0.6344512, 0.2857012, x, 12, 0.9370859, 0.9763552, 1.655159, 1.108178, 1.379591, 1.132953, 0.8454436, 0.8831299, 1.138031, 1.389878, 0.1810913, -0.0395423, -0.58678, -0.1507059, 0.2970802, 1.13504, 0.8643419, 0.6668617, 0.4453254, 1.041971, 0.8924299, 0.669918, -0.2561768, -0.2897301, -1.249867, -1.132152, -0.314629, -0.4366662, -0.161319, 0.5992076, 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-0.3334491, -0.6013179, -0.3337353, -0.6369814, -0.362383, 0.00711959, 0.267253, 0.09519942, 1.057406, 0.3189074, 0.1094272, -0.3849736, -0.4189788, 1.366315, -0.5023601, 1.031713, 0.2547609, 0.06090767, -1.057149, -0.5848955, -0.7751209, -1.435286, -1.393532, -0.3944534, 0.08009424, 0.5406277, 0.03648449, 0.2507267, -0.6927136, -1.187208, -1.317312, -1.27662, -1.73189, -0.3903396, -0.2579699, 0.5905425, -0.2050689, -1.342868, 0.2939729, -1.264092, -1.259792, -0.6294847, -1.302177, -0.3244287, -0.5285313, 0.8214888, 0.7168574, 0.470183, -0.1752786, -0.4075685, -0.1714465, function (x, ...) , {, return(ets(x, model = etsmodel, allow.multiplicative.trend = allow.multiplicative.trend, , ...)), }, 0.9906971, 1.151383, 1.175557, 1.259092, 1.090324, 0.2703676, 0.01450519, 0.4058997, 1.654422, 0.6482627, 0.03112553, 0.08045178, 0.2083044, -0.3394663, 2.358585, 0.300964, 0.52202, -0.08719498, 0.6205873, -0.10449, 0.5362684, 0.4058997, -0.002206715, -0.8857122, -1.058618, -0.5557404, 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0.4315781, -0.4876074, 0.7686349, 0.1255318, 1.403855, 0.4974218, -0.0333516, -0.6597647, -0.5152346, 0.01516009
## error
## 1 0.6480349
## 2 0.6685432
## 3 0.7172550
## 4 0.7370420
## 5 0.7389439
## 6 0.7455311
## forecast
## 1 17897.0000000, 17928.0000000, 17956.0000000, 17987.0000000, 18017.0000000, 18048.0000000, 18078.0000000, 18109.0000000, 18140.0000000, 18170.0000000, 18201.0000000, 18231.0000000, -0.5582366, -0.9486814, -1.1737968, -1.0454734, -0.7130640, -0.2474913, 0.2053904, 0.5459029, 0.7023439, 0.6620746, 0.4588411, 0.1626949
## 2 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.11230596, -0.73841382, -0.56469022, -0.54656448, -0.57546979, -0.45188893, 0.07649869, 0.23423356, 0.93161171, 0.88162146, 0.86952369, 0.21377625
## 3 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, -0.06524640, -0.85284900, -0.80805044, -0.44806597, -0.53266580, -0.44134274, -0.08682303, 0.13090410, 0.72503836, 0.95128131, 0.90038578, 0.21623900
## 4 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, -0.25904194, -0.59961311, -0.60154396, -0.43126522, -0.31890082, -0.31210849, -0.24576010, 0.04686422, 0.50490579, 0.83868463, 0.77426709, 0.31136403
## 5 17897.0000000, 17928.0000000, 17956.0000000, 17987.0000000, 18017.0000000, 18048.0000000, 18078.0000000, 18109.0000000, 18140.0000000, 18170.0000000, 18201.0000000, 18231.0000000, -0.3127971, -0.6298306, -0.5757757, -0.3507082, -0.2062148, -0.1845841, -0.1019946, 0.2143350, 0.6864909, 1.0012958, 0.8821511, 0.3565379
## 6 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, -0.39093589, -1.00474585, -0.83975164, -0.84358343, -0.59655608, -0.23095112, 0.06257842, -0.23583631, 0.73027645, 0.86608898, 0.52909763, -0.17645332
## key
## 1 ts-auto.arima
## 2 msts6-stlm
## 3 ts-stlm
## 4 msts6-tbats
## 5 ts-tbats
## 6 msts24-stlmharvest_data_jp_test_x <- harvest_data_jp_test %>%
select(IDX, key, actual = test, forecast) %>%
spread(key, forecast) %>%
gather(key, value, -IDX)
harvest_data_jp_test_x <- harvest_data_jp_test_x %>%
unnest(value) %>%
mutate(value = jp_revert(value, jp_recipe, IDX))
harvest_data_jp_test_x %>%
# filter(IDX == "AREA0001.2006.BINA_SAWIT.N") %>%
separate(IDX, c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM"), "-") %>%
filter(
TAHUN_TANAM >= 2006 & TAHUN_TANAM <= 2011 & GROUP_AREA == "AREA0001" & ( AREA == "A09" | AREA == "A10" | AREA == "A11")
) %>%
ggplot(aes(x = PERIOD, y = value, colour = key)) +
geom_line() +
labs(x = NULL, y = NULL, colour = NULL) +
facet_wrap(~ AREA, scale = "free", ncol = 1) +
theme_light()+
theme(legend.position = "bottom")+
theme(legend.title=element_text(size=8))+
theme(legend.text=element_text(size=6))+
tidyquant::scale_colour_tq()
jp_model_selected <- harvest_data_jp_nest_res %>%
group_by(IDX) %>%
filter(error == min(error)) %>%
ungroup() %>%
distinct_all()
head(jp_model_selected)## # A tibble: 6 x 9
## train test func_name func IDX model_name model fitted error
## <list<df[> <list<df[> <chr> <list> <chr> <chr> <lis> <list> <dbl>
## 1 [71 × 2] [12 × 2] ts <fn> AREA000… auto.arima <fn> <fr_AR… 0.648
## 2 [71 × 2] [12 × 2] ts <fn> AREA000… auto.arima <fn> <fr_AR… 0.537
## 3 [71 × 2] [12 × 2] ts <fn> AREA000… auto.arima <fn> <fr_AR… 0.428
## 4 [71 × 2] [12 × 2] ts <fn> AREA000… auto.arima <fn> <fr_AR… 0.632
## 5 [71 × 2] [12 × 2] msts24 <fn> AREA000… stlm <fn> <stlm> 0.505
## 6 [71 × 2] [12 × 2] msts24 <fn> AREA000… stlm <fn> <stlm> 0.727harvest_data_jp_test_x %>%
filter(
paste(IDX, key, sep = "-") %in%
paste(jp_model_selected$IDX,
jp_model_selected$func_name,
jp_model_selected$model_name,
sep = "-")
| key == "actual"
) %>%
separate(IDX, c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM"), "-") %>%
filter(
TAHUN_TANAM >= 2006 & TAHUN_TANAM <= 2011 & GROUP_AREA == "AREA0001" & ( AREA == "A09" | AREA == "A10" | AREA == "A11")
) %>%
ggplot(aes(x = PERIOD, y = value, colour = key)) +
geom_line() +
labs(x = NULL, y = NULL, colour = NULL) +
facet_wrap(~ AREA, scale = "free", ncol = 1) +
theme_light()+
theme(legend.position = "bottom")+
theme(legend.title=element_text(size=8))+
theme(legend.text=element_text(size=6))+
tidyquant::scale_colour_tq()
jp_mape <- harvest_data_jp_test_x %>%
filter(
paste(IDX, key, sep = "-") %in%
paste(jp_model_selected$IDX,
jp_model_selected$func_name,
jp_model_selected$model_name,
sep = "-")
| key == "actual"
) %>%
mutate(
value = as.numeric(value),
key = if_else(key == "actual", "actual", "forecast")
) %>%
distinct_all() %>%
# filter(key != "actual") %>%
spread(key, value)
# MAPE per IDX
jp_mape_by_IDX <- jp_mape %>%
group_by(IDX) %>%
nest() %>%
mutate(
tmp = map(data, ~invoke_map_dfc(list(mape = mape_vec),
truth = .x$actual, estimate = .x$forecast))
) %>%
select(-data) %>%
unnest() %>%
ungroup() %>%
add_row(IDX = "all",
mape = MLmetrics::MAPE(jp_mape$forecast, jp_mape$actual)*100) %>%
mutate(
mape = round(mape,4)
) %>%
arrange(IDX)
jp_mape_by_IDX## # A tibble: 66 x 2
## IDX mape
## <chr> <dbl>
## 1 all 23.0
## 2 AREA0001-A-A09-2006 30.9
## 3 AREA0001-A-A10-2006 29.6
## 4 AREA0001-A-A11-2006 12.6
## 5 AREA0001-A-A12-2006 27.7
## 6 AREA0001-A-A13-2006 16.7
## 7 AREA0001-A-A14-2006 30.3
## 8 AREA0001-A-A15-2006 24.4
## 9 AREA0001-A-A16-2006 27.7
## 10 AREA0001-A-A17-2006 18.9
## # … with 56 more rowsFORECAST RATA-RATA BERAT JANJANG
harvest_data_bjr <- harvest_data %>%
select(GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM, PERIOD, AVG_KG_JANJANG) %>%
mutate(
IDX = paste(GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM, sep = "-"),
value = AVG_KG_JANJANG
) %>%
ungroup() %>%
select(IDX, PERIOD, value) %>%
spread(IDX, value)
head(harvest_data_bjr)## # A tibble: 6 x 66
## PERIOD `AREA0001-A-A09… `AREA0001-A-A10… `AREA0001-A-A11… `AREA0001-A-A12…
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2013-02-01 9.06 8.77 9.74 9.57
## 2 2013-03-01 9.37 9.02 9.86 10.1
## 3 2013-04-01 8.59 7.99 9.82 9.57
## 4 2013-05-01 8.4 8.32 9.38 8.61
## 5 2013-06-01 7.59 8.38 9.3 8.68
## 6 2013-07-01 8.09 9.06 9.02 8.67
## # … with 61 more variables: `AREA0001-A-A13-2006` <dbl>,
## # `AREA0001-A-A14-2006` <dbl>, `AREA0001-A-A15-2006` <dbl>,
## # `AREA0001-A-A16-2006` <dbl>, `AREA0001-A-A17-2006` <dbl>,
## # `AREA0001-A-A18-2006` <dbl>, `AREA0001-A-A19-2006` <dbl>,
## # `AREA0001-A-AA12-2006` <dbl>, `AREA0001-A-B11-2006` <dbl>,
## # `AREA0001-A-B12-2006` <dbl>, `AREA0001-A-B13-2006` <dbl>,
## # `AREA0001-A-B14-2006` <dbl>, `AREA0001-A-B15-2006` <dbl>,
## # `AREA0001-A-B16-2006` <dbl>, `AREA0001-A-B17-2006` <dbl>,
## # `AREA0001-A-B18-2006` <dbl>, `AREA0001-B-C14-2006` <dbl>,
## # `AREA0001-B-C15-2006` <dbl>, `AREA0001-B-C16-2006` <dbl>,
## # `AREA0001-B-C17-2006` <dbl>, `AREA0001-B-C18-2006` <dbl>,
## # `AREA0001-B-D13-2006` <dbl>, `AREA0001-B-D14-2006` <dbl>,
## # `AREA0001-B-D15-2006` <dbl>, `AREA0001-B-D16-2006` <dbl>,
## # `AREA0001-B-D17-2006` <dbl>, `AREA0001-B-D18-2006` <dbl>,
## # `AREA0001-B-D19-2006` <dbl>, `AREA0001-B-E13-2006` <dbl>,
## # `AREA0001-B-E14-2006` <dbl>, `AREA0001-B-E15-2006` <dbl>,
## # `AREA0001-B-E16-2006` <dbl>, `AREA0001-B-E17-2006` <dbl>,
## # `AREA0001-H-I21-2006` <dbl>, `AREA0001-H-I22-2006` <dbl>,
## # `AREA0002-1-E21-2006` <dbl>, `AREA0002-1-E22-2006` <dbl>,
## # `AREA0002-1-E23-2006` <dbl>, `AREA0002-1-E24-2006` <dbl>,
## # `AREA0002-F-A20-2006` <dbl>, `AREA0002-F-A21-2006` <dbl>,
## # `AREA0002-F-A22-2006` <dbl>, `AREA0002-F-A23-2006` <dbl>,
## # `AREA0002-F-A24-2006` <dbl>, `AREA0002-F-A25-2006` <dbl>,
## # `AREA0002-F-A26-2006` <dbl>, `AREA0002-F-B20-2006` <dbl>,
## # `AREA0002-F-B21-2006` <dbl>, `AREA0002-F-B22-2006` <dbl>,
## # `AREA0002-F-B23-2006` <dbl>, `AREA0002-F-B24-2006` <dbl>,
## # `AREA0002-F-B25-2006` <dbl>, `AREA0002-F-B26-2006` <dbl>,
## # `AREA0002-F-C23-2006` <dbl>, `AREA0002-F-C25-2006` <dbl>,
## # `AREA0002-G-G21-2006` <dbl>, `AREA0002-G-G22-2006` <dbl>,
## # `AREA0002-G-H19-2006` <dbl>, `AREA0002-G-H20-2006` <dbl>,
## # `AREA0002-G-H21-2006` <dbl>, `AREA0002-G-H22-2006` <dbl># create recipe
bjr_recipe <- recipe(formula = ~.,
data = harvest_data_bjr) %>%
#step
step_sqrt(all_numeric()) %>% #melakukan akar
step_center(all_numeric()) %>% #melakukan pengurangan dengan nilai tengah
step_scale(all_numeric()) %>% #melakukan scaling data
#prep
prep()
# revert back function
bjr_revert <- function(vector, recipe, varname) {
# store recipe values
recipe_center <- recipe$steps[[2]]$means[varname]
recipe_scale <- recipe$steps[[3]]$sds[varname]
# convert back based on the recipe
results <- (vector * recipe_scale + recipe_center) ^ 2
# add additional adjustment if necessary
results <- round(results, 2)
# return the results
results
}# apply recipe
harvest_data_bjr_recipe <- bake(bjr_recipe, harvest_data_bjr)
# un-pivot data
harvest_data_bjr_recipe <- harvest_data_bjr_recipe %>%
gather(IDX, value, -PERIOD)
head(harvest_data_bjr_recipe)## # A tibble: 6 x 3
## PERIOD IDX value
## <date> <chr> <dbl>
## 1 2013-02-01 AREA0001-A-A09-2006 -1.14
## 2 2013-03-01 AREA0001-A-A09-2006 -1.04
## 3 2013-04-01 AREA0001-A-A09-2006 -1.29
## 4 2013-05-01 AREA0001-A-A09-2006 -1.36
## 5 2013-06-01 AREA0001-A-A09-2006 -1.63
## 6 2013-07-01 AREA0001-A-A09-2006 -1.46harvest_data_bjr_recipe <- harvest_data_bjr_recipe %>%
mutate(sample = case_when(
PERIOD %within% interval_train ~ "train",
PERIOD %within% interval_test ~ "test"
))
head(harvest_data_bjr_recipe)## # A tibble: 6 x 4
## PERIOD IDX value sample
## <date> <chr> <dbl> <chr>
## 1 2013-02-01 AREA0001-A-A09-2006 -1.14 train
## 2 2013-03-01 AREA0001-A-A09-2006 -1.04 train
## 3 2013-04-01 AREA0001-A-A09-2006 -1.29 train
## 4 2013-05-01 AREA0001-A-A09-2006 -1.36 train
## 5 2013-06-01 AREA0001-A-A09-2006 -1.63 train
## 6 2013-07-01 AREA0001-A-A09-2006 -1.46 train# cek tipe time series
check_tipe_ts <- harvest_data_bjr_recipe %>%
filter(IDX == "AREA0001-A-A09-2006" & sample == "train") %>%
select(PERIOD, value)
# single seasonal
ts_a <- ts(data = check_tipe_ts$value, frequency = 12)
ts_a %>% decompose() %>% autoplot()
# multi-seasonal
msts_a <- msts(data = check_tipe_ts$value, seasonal.periods = c(12, 12*2))
msts_a %>% mstl() %>% autoplot()
harvest_data_bjr_nest <- harvest_data_bjr_recipe %>%
group_by(IDX, sample) %>%
nest(.key = "data") %>%
pivot_wider(names_from = sample, values_from = data)
head(harvest_data_bjr_nest)## # A tibble: 6 x 3
## # Groups: IDX [6]
## IDX train test
## <chr> <list<df[,2]>> <list<df[,2]>>
## 1 AREA0001-A-A09-2006 [71 × 2] [12 × 2]
## 2 AREA0001-A-A10-2006 [71 × 2] [12 × 2]
## 3 AREA0001-A-A11-2006 [71 × 2] [12 × 2]
## 4 AREA0001-A-A12-2006 [71 × 2] [12 × 2]
## 5 AREA0001-A-A13-2006 [71 × 2] [12 × 2]
## 6 AREA0001-A-A14-2006 [71 × 2] [12 × 2]# list fungsi yang akan digunakan untuk membentuk time series
define_function <- list(
ts = function(x) ts(x$value, frequency = 12),
msts6 = function(x) msts(x$value, seasonal.periods = c(12, 6)),
msts18 = function(x) msts(x$value, seasonal.periods = c(12, 18)),
msts24 = function(x) msts(x$value, seasonal.periods = c(12, 24)),
msts30 = function(x) msts(x$value, seasonal.periods = c(12, 30))
)
function_list <- define_function %>%
rep(nrow(harvest_data_bjr_nest)) %>%
enframe("func_name", "func")
# replicate data harvest_data_bjr_nest
harvest_data_bjr_nest_rep <- harvest_data_bjr_nest[rep(seq_len(nrow(harvest_data_bjr_nest)), each = length(define_function)), ]
# kombinasikan function_list dengan masing-masing GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM
harvest_data_bjr_nest <- merge(harvest_data_bjr_nest_rep, function_list, all.x=TRUE)
harvest_data_bjr_nest <- harvest_data_bjr_nest %>%
separate(IDX, c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM"), "-") %>%
mutate(
IDX = paste(GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM, sep = "-")
) %>%
select(-c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM")) %>%
distinct_all() %>%
arrange(IDX)
head(harvest_data_bjr_nest)## train
## 1 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 2 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 3 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 4 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 5 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 6 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, -1.437500213, -1.337549623, -1.758864696, -1.621070225, -1.596312181, -1.321686685, -1.441527677, -0.790698507, -0.572477318, -0.259419519, -0.001212817, -0.021311650, -0.297464893, -0.536688512, -0.440852582, -0.943440066, -1.141720519, -1.022881858, -0.724589023, -1.246811505, -1.429452168, -1.103176844, -0.515292228, -0.409159541, -0.518854292, -0.249073955, -0.058283801, -0.558142442, -0.637307688, -0.568891170, -0.928402802, -1.670855590, -1.571642613, -0.835088276, -0.388099640, -0.451444664, -0.924648151, -1.313768347, -1.506282533, -1.353447774, -0.238741347, 0.508782258, 0.423151770, 0.901105409, 0.937331991, 0.767063039, 0.816028880, 0.901105409, 0.699309436, 0.680744398, 0.714751729, 1.231056580, 1.384716570, 1.538857030, 1.707394516, 1.519009643, 0.925271935, 0.949376654, 0.652826233, 1.657126921, 1.353021949, 1.631899599, 1.538857030, 1.416309885, 1.640315680, 0.400816793, 0.615468999, 0.680744398, 0.301223436, 0.148039574, 0.239621374
## test
## 1 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 2 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 3 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 4 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 5 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 6 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.77626382, 1.37896148, 0.91621676, 0.44543098, 0.74248283, 0.93733199, 0.64039079, 0.57482512, 0.21355337, 0.10188303, -0.06502342, 0.20049008
## func_name func
## 1 ts function (x) , ts(x$value, frequency = 12)
## 2 msts6 function (x) , msts(x$value, seasonal.periods = c(12, 6))
## 3 msts18 function (x) , msts(x$value, seasonal.periods = c(12, 18))
## 4 msts24 function (x) , msts(x$value, seasonal.periods = c(12, 24))
## 5 msts30 function (x) , msts(x$value, seasonal.periods = c(12, 30))
## 6 ts function (x) , ts(x$value, frequency = 12)
## IDX
## 1 AREA0001-A-A09-2006
## 2 AREA0001-A-A09-2006
## 3 AREA0001-A-A09-2006
## 4 AREA0001-A-A09-2006
## 5 AREA0001-A-A09-2006
## 6 AREA0001-A-A10-2006# list model yang akan digunakan untuk pemodelan time series
define_model <- list(
ets = function(x) ets(x),
auto.arima = function(x) auto.arima(x),
stlm = function(x) stlm(x),
tbats = function(x) tbats(x, use.box.cox = FALSE)
)
model_list <- define_model %>%
rep(nrow(harvest_data_bjr_nest)) %>%
enframe("model_name", "model")
# replicate data harvest_data_bjr_nest
harvest_data_bjr_nest_rep <- harvest_data_bjr_nest[rep(seq_len(nrow(harvest_data_bjr_nest)), each = length(define_model)), ]
# kombinasikan function_list dengan masing-masing GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM
harvest_data_bjr_nest <- merge(harvest_data_bjr_nest_rep, model_list, all.x=TRUE)
harvest_data_bjr_nest <- harvest_data_bjr_nest %>%
filter(
!(model_name == "ets" & stringr::str_detect(func_name,"msts")),
!(model_name == "auto.arima" & stringr::str_detect(func_name,"msts"))
) %>%
distinct_all() %>%
arrange(IDX)
head(harvest_data_bjr_nest)## train
## 1 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 2 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 3 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 4 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 5 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 6 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## test
## 1 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 2 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 3 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 4 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 5 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 6 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## func_name func
## 1 ts function (x) , ts(x$value, frequency = 12)
## 2 ts function (x) , ts(x$value, frequency = 12)
## 3 ts function (x) , ts(x$value, frequency = 12)
## 4 msts6 function (x) , msts(x$value, seasonal.periods = c(12, 6))
## 5 msts18 function (x) , msts(x$value, seasonal.periods = c(12, 18))
## 6 msts24 function (x) , msts(x$value, seasonal.periods = c(12, 24))
## IDX model_name model
## 1 AREA0001-A-A09-2006 ets function (x) , ets(x)
## 2 AREA0001-A-A09-2006 auto.arima function (x) , auto.arima(x)
## 3 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 4 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 5 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 6 AREA0001-A-A09-2006 stlm function (x) , stlm(x)harvest_data_bjr_nest_res <- harvest_data_bjr_nest %>%
mutate(
params = map(train, ~ list(x = .x)),
data = invoke_map(func, params),
params = map(data, ~ list(x = .x)),
fitted = invoke_map(model, params)
) %>%
select(-data, -params)harvest_data_bjr_nest_res <- harvest_data_bjr_nest_res %>%
mutate(
error = map(fitted, ~ forecast(.x, h = 12)) %>%
map2_dbl(test, ~ rmse_vec(truth = .y$value, estimate = .x$mean))
) %>%
arrange(IDX, error)
harvest_data_bjr_nest_res %>%
select(IDX, ends_with("_name"), error) %>%
head()## IDX func_name model_name error
## 1 AREA0001-A-A09-2006 msts24 stlm 0.6858544
## 2 AREA0001-A-A09-2006 ts stlm 0.8115605
## 3 AREA0001-A-A09-2006 msts30 stlm 0.8347034
## 4 AREA0001-A-A09-2006 msts6 stlm 0.8506618
## 5 AREA0001-A-A09-2006 ts auto.arima 0.8524453
## 6 AREA0001-A-A09-2006 ts tbats 0.8543327harvest_data_bjr_test <- harvest_data_bjr_nest_res %>%
mutate(
forecast = map(fitted, ~ forecast(.x, h = 12)) %>%
map2(test, ~ tibble(PERIOD = .y$PERIOD, value = as.vector(.x$mean))),
key = paste(func_name, model_name, sep = "-")
)
head(harvest_data_bjr_test)## train
## 1 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 2 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 3 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 4 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 5 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## 6 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, -1.13782588, -1.03850988, -1.29170115, -1.35509813, -1.63377765, -1.46009477, -1.59158433, -1.03850988, -0.82625595, -0.31944730, -0.35070180, -0.33647702, -0.49757398, -0.76520505, -0.59440533, -1.34169229, -1.43621241, -1.14752666, -0.88791741, -1.16697670, -1.19301156, -0.88172326, -0.24054322, -0.14871695, -0.20979632, -0.10189008, 0.05821439, -0.24334534, -0.25175868, -0.08817791, -0.22655004, -0.86935377, -0.99419650, -0.79260356, -0.68972300, -0.92521509, -1.04804858, -1.30166425, -1.44984687, -1.07995540, -0.31944730, 0.22263234, 0.11433037, 0.67263692, 0.81203209, 1.04604515, 0.92990273, 0.94898078, 0.70710814, 0.74875968, 0.69973445, 1.33044887, 1.20218832, 1.37803989, 1.62961623, 1.14660078, 0.87239090, 0.88920854, 0.44468038, 1.28029796, 1.16749048, 1.64495958, 1.68864831, 1.42311446, 2.82760104, 0.72183436, 0.47767152, 0.40131601, 0.03940954, 0.08499177, 0.11699151
## test
## 1 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 2 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 3 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 4 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 5 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## 6 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.49537577, 1.06719549, 1.01303354, 0.99647658, 2.22549879, 0.76340682, 0.14884785, 0.27227359, -0.06629491, -0.12113308, -0.52968988, -0.71980682
## func_name func
## 1 msts24 function (x) , msts(x$value, seasonal.periods = c(12, 24))
## 2 ts function (x) , ts(x$value, frequency = 12)
## 3 msts30 function (x) , msts(x$value, seasonal.periods = c(12, 30))
## 4 msts6 function (x) , msts(x$value, seasonal.periods = c(12, 6))
## 5 ts function (x) , ts(x$value, frequency = 12)
## 6 ts function (x) , ts(x$value, frequency = 12)
## IDX model_name model
## 1 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 2 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 3 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 4 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 5 AREA0001-A-A09-2006 auto.arima function (x) , auto.arima(x)
## 6 AREA0001-A-A09-2006 tbats function (x) , tbats(x, use.box.cox = FALSE)
## fitted
## 1 -1.137826, -1.03851, -1.291701, -1.355098, -1.633778, -1.460095, -1.591584, -1.03851, -0.826256, -0.3194473, -0.3507018, -0.336477, -0.497574, -0.7652051, -0.5944053, -1.341692, -1.436212, -1.147527, -0.8879174, -1.166977, -1.193012, -0.8817233, -0.2405432, -0.1487169, -0.2097963, -0.1018901, 0.05821439, -0.2433453, -0.2517587, -0.08817791, -0.22655, -0.8693538, -0.9941965, -0.7926036, -0.689723, -0.9252151, -1.048049, -1.301664, -1.449847, -1.079955, -0.3194473, 0.2226323, 0.1143304, 0.6726369, 0.8120321, 1.046045, 0.9299027, 0.9489808, 0.7071081, 0.7487597, 0.6997345, 1.330449, 1.202188, 1.37804, 1.629616, 1.146601, 0.8723909, 0.8892085, 0.4446804, 1.280298, 1.16749, 1.64496, 1.688648, 1.423114, 2.827601, 0.7218344, 0.4776715, 0.401316, 0.03940954, 0.08499177, 0.1169915, -1.334098, -1.298085, -1.262072, -1.226059, -1.190046, -1.154033, -1.120243, -1.086454, -1.052665, -1.018875, -0.9850858, -0.951502, -0.9179181, -0.8843343, -0.8507505, -0.8171666, -0.7852122, -0.7532577, 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## 5 0.1883206, -40.88936, 83.77873, 0, 0, 0, 0, 12, 1, 0, -0.001137825, 0.099316, -0.2531913, -0.06339698, -0.2786795, 0.1736829, -0.1314896, 0.5530745, 0.2122539, 0.5068086, -0.0312545, 0.01422479, -0.161097, -0.2676311, 0.1707997, -0.747287, -0.09452012, 0.2886857, 0.2596093, -0.2790593, -0.02603486, 0.3112883, 0.64118, 0.09182627, -0.06107938, 0.1079062, 0.1601045, -0.3015597, -0.008413344, 0.1635808, -0.1383721, -0.6428037, -0.1248427, 0.2015929, 0.1028806, -0.2354921, -0.1228335, -0.2536157, -0.1481826, 0.3698915, 0.7605081, 0.5420796, -0.108302, 0.5583065, 0.1393952, 0.2340131, -0.1161424, 0.01907805, -0.2418726, 0.04165155, -0.04902523, 0.6307144, -0.1282605, 0.1758516, 0.2515763, -0.4830154, -0.2742099, 0.01681763, -0.4445282, 0.8356176, -0.1128075, 0.4774691, 0.04368873, -0.2655339, 1.404487, -2.105767, -0.2441628, -0.07635551, -0.3619065, 0.04558223, 0.03199973, auto.arima(y = x, x = structure(list(x = structure(c(-1.13782587728858, , -1.03850987651194, -1.29170115303331, -1.35509812821782, -1.63377765390487, , -1.46009476555517, -1.59158433322581, -1.03850987651194, -0.826255953051835, , -0.319447303290611, -0.350701804590243, -0.336477018874338, -0.497573975144439, , -0.765205051377092, -0.594405327340667, -1.34169228641192, -1.43621240773445, , -1.14752666401844, -0.88791740931199, -1.16697670126058, -1.19301155757402, , -0.881723263388007, -0.24054321782938, -0.148716946729809, -0.209796324060438, , -0.101890075321211, 0.0582143863205467, -0.243345335994908, -0.251758679773735, , -0.0881779093774298, -0.226550035089692, -0.869353770195645, , -0.994196498051947, -0.792603558440404, -0.689723003385417, -0.925215085796861, , -1.04804857973331, -1.30166424627691, -1.44984686813722, -1.0799554020582, , -0.319447303290611, 0.222632336685654, 0.114330367844773, 0.672636916479899, , 0.812032087105972, 1.0460451465864, 0.929902730487705, 0.948980781901182, , 0.707108137573573, 0.748759684552217, 0.699734452129928, 1.33044886603872, , 1.20218831671963, 1.37803988861521, 1.6296162279439, 1.14660077875508, , 0.872390903727802, 0.889208537979074, 0.444680384442321, 1.28029795713605, , 1.16749048282297, 1.64495958319105, 1.68864831023853, 1.42311445541621, , 2.82760104379708, 0.721834363558796, 0.477671521108767, 0.401316008756799, , 0.0394095415415868, 0.0849917748882819, 0.116991508928389), .Tsp = c(1, , 6.83333333333333, 12), class = "ts")), class = "data.frame", row.names = c(NA, , -71L))), x, 0, 0, 70, 1, 1, 1, 0.03199973, 0.08499177, 0, -0.00000000000000000000007420876, -0.00000000000000000000007420876, 0.00000000000000000000007420876, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0.00000000000000000000007420876, 86.02722, 83.83755, -1.137826, -1.03851, -1.291701, -1.355098, -1.633778, -1.460095, -1.591584, -1.03851, -0.826256, -0.3194473, -0.3507018, -0.336477, -0.497574, -0.7652051, -0.5944053, -1.341692, -1.436212, -1.147527, -0.8879174, -1.166977, -1.193012, -0.8817233, -0.2405432, -0.1487169, -0.2097963, -0.1018901, 0.05821439, -0.2433453, -0.2517587, -0.08817791, -0.22655, -0.8693538, -0.9941965, -0.7926036, -0.689723, -0.9252151, -1.048049, -1.301664, -1.449847, -1.079955, -0.3194473, 0.2226323, 0.1143304, 0.6726369, 0.8120321, 1.046045, 0.9299027, 0.9489808, 0.7071081, 0.7487597, 0.6997345, 1.330449, 1.202188, 1.37804, 1.629616, 1.146601, 0.8723909, 0.8892085, 0.4446804, 1.280298, 1.16749, 1.64496, 1.688648, 1.423114, 2.827601, 0.7218344, 0.4776715, 0.401316, 0.03940954, 0.08499177, 0.1169915, -1.136688, -1.137826, -1.03851, -1.291701, -1.355098, -1.633778, -1.460095, -1.591584, -1.03851, -0.826256, -0.3194473, -0.3507018, -0.336477, -0.497574, -0.7652051, -0.5944053, -1.341692, -1.436212, -1.147527, -0.8879174, -1.166977, -1.193012, -0.8817233, -0.2405432, -0.1487169, -0.2097963, -0.1018901, 0.05821439, -0.2433453, -0.2517587, -0.08817791, -0.22655, -0.8693538, -0.9941965, -0.7926036, -0.689723, -0.9252151, -1.048049, -1.301664, -1.449847, -1.079955, -0.3194473, 0.2226323, 0.1143304, 0.6726369, 0.8120321, 1.046045, 0.9299027, 0.9489808, 0.7071081, 0.7487597, 0.6997345, 1.330449, 1.202188, 1.37804, 1.629616, 1.146601, 0.8723909, 0.8892085, 0.4446804, 1.280298, 1.16749, 1.64496, 1.688648, 1.423114, 2.827601, 0.7218344, 0.4776715, 0.401316, 0.03940954, 0.08499177
## 6 0.8437758, 181.2009, 0, 0.1807655, 185.2009, 0.8437758, FALSE, FALSE, FALSE, 0, 0, 0, -0.9707889, -0.9707889, -1.111731, -1.049949, -1.253934, -1.339294, -1.587772, -1.480041, -1.574159, -1.122191, -0.8724882, -0.4058457, -0.3593166, -0.3400451, -0.4729642, -0.71955, -0.6139559, -1.228002, -1.403685, -1.187545, -0.9347265, -1.130694, -1.183276, -0.9288331, -0.3480707, -0.1798608, -0.2051197, -0.118017, 0.03068278, -0.2005355, -0.2437564, -0.112483, -0.20873, -0.7661484, -0.9585699, -0.8185315, -0.709846, -0.8915692, -1.023603, -1.258224, -1.419911, -1.133065, -0.446554, 0.1180892, 0.1149176, 0.5855077, 0.7766435, 1.003958, 0.941472, 0.9478077, 0.7447112, 0.7481272, 0.7072946, 1.233097, 1.207017, 1.351322, 1.58614, 1.215267, 0.9259565, 0.8949495, 0.5150233, 1.160744, 1.166436, 1.570203, 1.670144, 1.461706, 2.614215, 1.01747, 0.5620011, 0.4264189, 0.09986977, 0.08731608, -0.1670369, 0.07322079, -0.2417524, -0.1011646, -0.2944839, 0.1276774, -0.1115433, 0.5356487, 0.2959352, 0.5530409, 0.05514386, 0.02283959, -0.1575289, -0.2922409, 0.1251446, -0.7277363, -0.2082101, 0.2561583, 0.2996274, -0.2322502, -0.06231796, 0.3015527, 0.6882899, 0.1993538, -0.02993549, 0.1032296, 0.1762314, -0.2740281, -0.05122316, 0.1555785, -0.114067, -0.6606238, -0.2280481, 0.1659663, 0.1288085, -0.2153691, -0.1564794, -0.2780615, -0.1916226, 0.3399554, 0.8136174, 0.6691863, -0.003758876, 0.5577193, 0.2265244, 0.2694017, -0.07405536, 0.007508813, -0.2406996, 0.00404845, -0.04839277, 0.6231543, -0.03090878, 0.1710229, 0.2782942, -0.4395392, -0.3428765, -0.03674797, -0.4502691, 0.7652747, 0.006746936, 0.4785231, 0.1184456, -0.2470298, 1.365895, -1.892381, -0.5397985, -0.1606851, -0.3870094, -0.01487799, 0.02967543, -1.111731, -1.049949, -1.253934, -1.339294, -1.587772, -1.480041, -1.574159, -1.122191, -0.8724882, -0.4058457, -0.3593166, -0.3400451, -0.4729642, -0.71955, -0.6139559, -1.228002, -1.403685, -1.187545, -0.9347265, -1.130694, -1.183276, -0.9288331, -0.3480707, -0.1798608, -0.2051197, -0.118017, 0.03068278, -0.2005355, -0.2437564, -0.112483, -0.20873, -0.7661484, -0.9585699, -0.8185315, -0.709846, -0.8915692, -1.023603, -1.258224, -1.419911, -1.133065, -0.446554, 0.1180892, 0.1149176, 0.5855077, 0.7766435, 1.003958, 0.941472, 0.9478077, 0.7447112, 0.7481272, 0.7072946, 1.233097, 1.207017, 1.351322, 1.58614, 1.215267, 0.9259565, 0.8949495, 0.5150233, 1.160744, 1.166436, 1.570203, 1.670144, 1.461706, 2.614215, 1.01747, 0.5620011, 0.4264189, 0.09986977, 0.08731608, 0.1123555, -1.137826, -1.03851, -1.291701, -1.355098, -1.633778, -1.460095, -1.591584, -1.03851, -0.826256, -0.3194473, -0.3507018, -0.336477, -0.497574, -0.7652051, -0.5944053, -1.341692, -1.436212, -1.147527, -0.8879174, -1.166977, -1.193012, -0.8817233, -0.2405432, -0.1487169, -0.2097963, -0.1018901, 0.05821439, -0.2433453, -0.2517587, -0.08817791, -0.22655, -0.8693538, -0.9941965, -0.7926036, -0.689723, -0.9252151, -1.048049, -1.301664, -1.449847, -1.079955, -0.3194473, 0.2226323, 0.1143304, 0.6726369, 0.8120321, 1.046045, 0.9299027, 0.9489808, 0.7071081, 0.7487597, 0.6997345, 1.330449, 1.202188, 1.37804, 1.629616, 1.146601, 0.8723909, 0.8892085, 0.4446804, 1.280298, 1.16749, 1.64496, 1.688648, 1.423114, 2.827601, 0.7218344, 0.4776715, 0.401316, 0.03940954, 0.08499177, 0.1169915, tbats(y = x, use.box.cox = FALSE), x, TBATS
## error
## 1 0.6858544
## 2 0.8115605
## 3 0.8347034
## 4 0.8506618
## 5 0.8524453
## 6 0.8543327
## forecast
## 1 17897.0000000000, 17928.0000000000, 17956.0000000000, 17987.0000000000, 18017.0000000000, 18048.0000000000, 18078.0000000000, 18109.0000000000, 18140.0000000000, 18170.0000000000, 18201.0000000000, 18231.0000000000, 0.6343564425, 0.3281566758, 0.4183847701, 0.3434718887, 0.4818015729, 0.4201770615, 0.4417008750, 0.4145018777, 0.1355315379, 0.0008429842, 0.1740623922, -0.0240472960
## 2 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.32740483, 0.15746198, 0.18399785, 0.16206125, 0.12687404, 0.48082158, 0.25964658, 0.22390807, 0.09854743, -0.02986576, 0.13106714, 0.11289296
## 3 17897.0000000, 17928.0000000, 17956.0000000, 17987.0000000, 18017.0000000, 18048.0000000, 18078.0000000, 18109.0000000, 18140.0000000, 18170.0000000, 18201.0000000, 18231.0000000, 1.0333932, 0.7883057, 1.0160213, 1.2005214, 0.8159292, 0.9169991, 0.7308112, 0.7809431, 0.5186903, 0.3523161, 0.8912026, 0.9411183
## 4 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.21177023, 0.03799577, 0.11203056, 0.05402718, 0.06321933, 0.50605846, 0.15024574, 0.11188329, 0.02872559, -0.12855730, 0.06802976, 0.12283493
## 5 17897.0000000, 17928.0000000, 17956.0000000, 17987.0000000, 18017.0000000, 18048.0000000, 18078.0000000, 18109.0000000, 18140.0000000, 18170.0000000, 18201.0000000, 18231.0000000, 0.1169915, 0.1169915, 0.1169915, 0.1169915, 0.1169915, 0.1169915, 0.1169915, 0.1169915, 0.1169915, 0.1169915, 0.1169915, 0.1169915
## 6 17897.0000000, 17928.0000000, 17956.0000000, 17987.0000000, 18017.0000000, 18048.0000000, 18078.0000000, 18109.0000000, 18140.0000000, 18170.0000000, 18201.0000000, 18231.0000000, 0.1123555, 0.1123555, 0.1123555, 0.1123555, 0.1123555, 0.1123555, 0.1123555, 0.1123555, 0.1123555, 0.1123555, 0.1123555, 0.1123555
## key
## 1 msts24-stlm
## 2 ts-stlm
## 3 msts30-stlm
## 4 msts6-stlm
## 5 ts-auto.arima
## 6 ts-tbatsharvest_data_bjr_test_x <- harvest_data_bjr_test %>%
select(IDX, key, actual = test, forecast) %>%
spread(key, forecast) %>%
gather(key, value, -IDX)
harvest_data_bjr_test_x <- harvest_data_bjr_test_x %>%
unnest(value) %>%
mutate(value = bjr_revert(value, bjr_recipe, IDX))
harvest_data_bjr_test_x %>%
# filter(IDX == "AREA0001.2006.BINA_SAWIT.N") %>%
separate(IDX, c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM"), "-") %>%
filter(
TAHUN_TANAM >= 2006 & TAHUN_TANAM <= 2011 & GROUP_AREA == "AREA0001" & ( AREA == "A09" | AREA == "A10" | AREA == "A11")
) %>%
ggplot(aes(x = PERIOD, y = value, colour = key)) +
geom_line() +
labs(x = NULL, y = NULL, colour = NULL) +
facet_wrap(~ AREA, scale = "free", ncol = 1) +
theme_light()+
theme(legend.position = "bottom")+
theme(legend.title=element_text(size=8))+
theme(legend.text=element_text(size=6))+
tidyquant::scale_colour_tq()
bjr_model_selected <- harvest_data_bjr_nest_res %>%
group_by(IDX) %>%
filter(error == min(error)) %>%
ungroup() %>%
distinct_all()
head(bjr_model_selected)## # A tibble: 6 x 9
## train test func_name func IDX model_name model fitted error
## <list<df[> <list<df[> <chr> <list> <chr> <chr> <lis> <list> <dbl>
## 1 [71 × 2] [12 × 2] msts24 <fn> AREA0001… stlm <fn> <stlm> 0.686
## 2 [71 × 2] [12 × 2] msts24 <fn> AREA0001… stlm <fn> <stlm> 0.427
## 3 [71 × 2] [12 × 2] msts24 <fn> AREA0001… stlm <fn> <stlm> 0.346
## 4 [71 × 2] [12 × 2] msts24 <fn> AREA0001… stlm <fn> <stlm> 0.275
## 5 [71 × 2] [12 × 2] msts24 <fn> AREA0001… stlm <fn> <stlm> 0.402
## 6 [71 × 2] [12 × 2] msts30 <fn> AREA0001… stlm <fn> <stlm> 0.552harvest_data_bjr_test_x %>%
filter(
paste(IDX, key, sep = "-") %in%
paste(bjr_model_selected$IDX,
bjr_model_selected$func_name,
bjr_model_selected$model_name,
sep = "-")
| key == "actual"
) %>%
separate(IDX, c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM"), "-") %>%
filter(
TAHUN_TANAM >= 2006 & TAHUN_TANAM <= 2011 & GROUP_AREA == "AREA0001" & ( AREA == "A09" | AREA == "A10" | AREA == "A11")
) %>%
ggplot(aes(x = PERIOD, y = value, colour = key)) +
geom_line() +
labs(x = NULL, y = NULL, colour = NULL) +
facet_wrap(~ AREA, scale = "free", ncol = 1) +
theme_light()+
theme(legend.position = "bottom")+
theme(legend.title=element_text(size=8))+
theme(legend.text=element_text(size=6))+
tidyquant::scale_colour_tq()
bjr_mape <- harvest_data_bjr_test_x %>%
filter(
paste(IDX, key, sep = "-") %in%
paste(bjr_model_selected$IDX,
bjr_model_selected$func_name,
bjr_model_selected$model_name,
sep = "-")
| key == "actual"
) %>%
mutate(
value = as.numeric(value),
key = if_else(key == "actual", "actual", "forecast")
) %>%
distinct_all() %>%
# filter(key != "actual") %>%
spread(key, value)
# MAPE per IDX
bjr_mape_by_IDX <- bjr_mape %>%
group_by(IDX) %>%
nest() %>%
mutate(
tmp = map(data, ~invoke_map_dfc(list(mape = mape_vec),
truth = .x$actual, estimate = .x$forecast))
) %>%
select(-data) %>%
unnest() %>%
ungroup() %>%
add_row(IDX = "all",
mape = MLmetrics::MAPE(bjr_mape$forecast, bjr_mape$actual)*100) %>%
mutate(
mape = round(mape,4)
) %>%
arrange(IDX)
bjr_mape_by_IDX## # A tibble: 66 x 2
## IDX mape
## <chr> <dbl>
## 1 all 8.90
## 2 AREA0001-A-A09-2006 13.5
## 3 AREA0001-A-A10-2006 6.59
## 4 AREA0001-A-A11-2006 5.84
## 5 AREA0001-A-A12-2006 4.61
## 6 AREA0001-A-A13-2006 6.08
## 7 AREA0001-A-A14-2006 12.0
## 8 AREA0001-A-A15-2006 8.68
## 9 AREA0001-A-A16-2006 5.95
## 10 AREA0001-A-A17-2006 6.96
## # … with 56 more rows** MENGHITUNG FORECAST PRODUKSI BERDASARKAN FORECAST JANJANG PER POKOK DAN FORECAST RATA-RATA BERAT JANJANG
forecast_jp <- harvest_data_jp_test_x %>%
filter(
paste(IDX, key, sep = "-") %in%
paste(jp_model_selected$IDX,
jp_model_selected$func_name,
jp_model_selected$model_name,
sep = "-")
) %>%
mutate(
janjang_per_pokok = as.numeric(value)
) %>%
arrange(IDX) %>%
distinct(IDX, PERIOD, .keep_all = TRUE)
forecast_bjr <- harvest_data_bjr_test_x %>%
filter(
paste(IDX, key, sep = "-") %in%
paste(bjr_model_selected$IDX,
bjr_model_selected$func_name,
bjr_model_selected$model_name,
sep = "-")
) %>%
mutate(
bjr = as.numeric(value)
) %>%
arrange(IDX) %>%
distinct(IDX, PERIOD, .keep_all = TRUE)
harvest_data_forecast <- harvest_data %>%
select(GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM, PERIOD, TON_PRODUKSI, POKOK_TANAM, JANJANG_PRODUKSI) %>%
mutate(
IDX = paste(GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM, sep = "-"),
actual = TON_PRODUKSI
) %>%
ungroup() %>%
select(IDX, PERIOD, actual, POKOK_TANAM) %>%
filter(PERIOD %within% interval_test)
harvest_data_forecast <- harvest_data_forecast %>%
left_join(forecast_jp, harvest_data_forecast, by = c("IDX", "PERIOD")) %>%
select(IDX, PERIOD, actual, POKOK_TANAM, janjang_per_pokok) %>%
mutate(janjang = janjang_per_pokok * POKOK_TANAM) %>%
left_join(forecast_bjr, harvest_data_forecast, by = c("IDX", "PERIOD")) %>%
select(-c(key,value)) %>%
mutate(forecast = janjang * bjr / 1000) %>%
select(IDX, PERIOD, actual, forecast) %>%
gather(key, value, -c(IDX,PERIOD))
harvest_data_forecast %>%
separate(IDX, c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM"), "-") %>%
filter(
TAHUN_TANAM >= 2006 & TAHUN_TANAM <= 2011 & GROUP_AREA == "AREA0001" & ( AREA == "A09" | AREA == "A10" | AREA == "A11")
) %>%
ggplot(aes(x = PERIOD, y = value, colour = key)) +
geom_line() +
labs(x = NULL, y = NULL, colour = NULL) +
facet_wrap(~ AREA, scale = "free", ncol = 1) +
theme_light()+
theme(legend.position = "bottom")+
theme(legend.title=element_text(size=8))+
theme(legend.text=element_text(size=6))+
tidyquant::scale_colour_tq()
## # A tibble: 66 x 2
## IDX mape
## <chr> <dbl>
## 1 all 23.8
## 2 AREA0001-A-A09-2006 36.4
## 3 AREA0001-A-A10-2006 27.6
## 4 AREA0001-A-A11-2006 13.1
## 5 AREA0001-A-A12-2006 25.9
## 6 AREA0001-A-A13-2006 17.1
## 7 AREA0001-A-A14-2006 35.8
## 8 AREA0001-A-A15-2006 19.0
## 9 AREA0001-A-A16-2006 23.5
## 10 AREA0001-A-A17-2006 17.8
## # … with 56 more rowsForecast Berdasarkan Historical Produksi
harvest_data_ton <- harvest_data %>%
select(GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM, PERIOD, TON_PRODUKSI) %>%
mutate(
value = TON_PRODUKSI,
IDX = paste(GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM, sep = "-")
) %>%
ungroup() %>%
select(IDX, PERIOD, value) %>%
spread(IDX, value)
head(harvest_data_ton)## # A tibble: 6 x 66
## PERIOD `AREA0001-A-A09… `AREA0001-A-A10… `AREA0001-A-A11… `AREA0001-A-A12…
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2013-02-01 9.01 35.3 69.3 81.1
## 2 2013-03-01 10.1 71.6 73.0 64.3
## 3 2013-04-01 9.35 36.5 64.0 59.2
## 4 2013-05-01 9.52 37.2 45.2 67.4
## 5 2013-06-01 7.94 34.2 39.7 42.8
## 6 2013-07-01 5.44 17.2 30.6 38.0
## # … with 61 more variables: `AREA0001-A-A13-2006` <dbl>,
## # `AREA0001-A-A14-2006` <dbl>, `AREA0001-A-A15-2006` <dbl>,
## # `AREA0001-A-A16-2006` <dbl>, `AREA0001-A-A17-2006` <dbl>,
## # `AREA0001-A-A18-2006` <dbl>, `AREA0001-A-A19-2006` <dbl>,
## # `AREA0001-A-AA12-2006` <dbl>, `AREA0001-A-B11-2006` <dbl>,
## # `AREA0001-A-B12-2006` <dbl>, `AREA0001-A-B13-2006` <dbl>,
## # `AREA0001-A-B14-2006` <dbl>, `AREA0001-A-B15-2006` <dbl>,
## # `AREA0001-A-B16-2006` <dbl>, `AREA0001-A-B17-2006` <dbl>,
## # `AREA0001-A-B18-2006` <dbl>, `AREA0001-B-C14-2006` <dbl>,
## # `AREA0001-B-C15-2006` <dbl>, `AREA0001-B-C16-2006` <dbl>,
## # `AREA0001-B-C17-2006` <dbl>, `AREA0001-B-C18-2006` <dbl>,
## # `AREA0001-B-D13-2006` <dbl>, `AREA0001-B-D14-2006` <dbl>,
## # `AREA0001-B-D15-2006` <dbl>, `AREA0001-B-D16-2006` <dbl>,
## # `AREA0001-B-D17-2006` <dbl>, `AREA0001-B-D18-2006` <dbl>,
## # `AREA0001-B-D19-2006` <dbl>, `AREA0001-B-E13-2006` <dbl>,
## # `AREA0001-B-E14-2006` <dbl>, `AREA0001-B-E15-2006` <dbl>,
## # `AREA0001-B-E16-2006` <dbl>, `AREA0001-B-E17-2006` <dbl>,
## # `AREA0001-H-I21-2006` <dbl>, `AREA0001-H-I22-2006` <dbl>,
## # `AREA0002-1-E21-2006` <dbl>, `AREA0002-1-E22-2006` <dbl>,
## # `AREA0002-1-E23-2006` <dbl>, `AREA0002-1-E24-2006` <dbl>,
## # `AREA0002-F-A20-2006` <dbl>, `AREA0002-F-A21-2006` <dbl>,
## # `AREA0002-F-A22-2006` <dbl>, `AREA0002-F-A23-2006` <dbl>,
## # `AREA0002-F-A24-2006` <dbl>, `AREA0002-F-A25-2006` <dbl>,
## # `AREA0002-F-A26-2006` <dbl>, `AREA0002-F-B20-2006` <dbl>,
## # `AREA0002-F-B21-2006` <dbl>, `AREA0002-F-B22-2006` <dbl>,
## # `AREA0002-F-B23-2006` <dbl>, `AREA0002-F-B24-2006` <dbl>,
## # `AREA0002-F-B25-2006` <dbl>, `AREA0002-F-B26-2006` <dbl>,
## # `AREA0002-F-C23-2006` <dbl>, `AREA0002-F-C25-2006` <dbl>,
## # `AREA0002-G-G21-2006` <dbl>, `AREA0002-G-G22-2006` <dbl>,
## # `AREA0002-G-H19-2006` <dbl>, `AREA0002-G-H20-2006` <dbl>,
## # `AREA0002-G-H21-2006` <dbl>, `AREA0002-G-H22-2006` <dbl># create recipe
ton_recipe <- recipe(formula = ~.,
data = harvest_data_ton) %>%
#step
step_sqrt(all_numeric()) %>% #melakukan akar
step_center(all_numeric()) %>% #melakukan pengurangan dengan nilai tengah
step_scale(all_numeric()) %>% #melakukan scaling data
#prep
prep()
# revert back function
ton_revert <- function(vector, recipe, varname) {
# store recipe values
recipe_center <- recipe$steps[[2]]$means[varname]
recipe_scale <- recipe$steps[[3]]$sds[varname]
# convert back based on the recipe
results <- (vector * recipe_scale + recipe_center) ^ 2
# add additional adjustment if necessary
results <- round(results, 2)
# return the results
results
}# apply recipe
harvest_data_ton_recipe <- bake(ton_recipe, harvest_data_ton)
# un-pivot data
harvest_data_ton_recipe <- harvest_data_ton_recipe %>%
gather(IDX, value, -PERIOD)
head(harvest_data_ton_recipe)## # A tibble: 6 x 3
## PERIOD IDX value
## <date> <chr> <dbl>
## 1 2013-02-01 AREA0001-A-A09-2006 0.440
## 2 2013-03-01 AREA0001-A-A09-2006 0.646
## 3 2013-04-01 AREA0001-A-A09-2006 0.507
## 4 2013-05-01 AREA0001-A-A09-2006 0.539
## 5 2013-06-01 AREA0001-A-A09-2006 0.221
## 6 2013-07-01 AREA0001-A-A09-2006 -0.354harvest_data_ton_recipe <- harvest_data_ton_recipe %>%
mutate(sample = case_when(
PERIOD %within% interval_train ~ "train",
PERIOD %within% interval_test ~ "test"
))
head(harvest_data_ton_recipe)## # A tibble: 6 x 4
## PERIOD IDX value sample
## <date> <chr> <dbl> <chr>
## 1 2013-02-01 AREA0001-A-A09-2006 0.440 train
## 2 2013-03-01 AREA0001-A-A09-2006 0.646 train
## 3 2013-04-01 AREA0001-A-A09-2006 0.507 train
## 4 2013-05-01 AREA0001-A-A09-2006 0.539 train
## 5 2013-06-01 AREA0001-A-A09-2006 0.221 train
## 6 2013-07-01 AREA0001-A-A09-2006 -0.354 train# cek tipe time series
check_tipe_ts <- harvest_data_ton_recipe %>%
filter(IDX == "AREA0001-A-A09-2006" & sample == "train") %>%
select(PERIOD, value)
# single seasonal
ts_a <- ts(data = check_tipe_ts$value, frequency = 12)
ts_a %>% decompose() %>% autoplot()
# multi-seasonal
msts_a <- msts(data = check_tipe_ts$value, seasonal.periods = c(12, 12*2))
msts_a %>% mstl() %>% autoplot()
harvest_data_ton_nest <- harvest_data_ton_recipe %>%
group_by(IDX, sample) %>%
nest(.key = "data") %>%
pivot_wider(names_from = sample, values_from = data)
head(harvest_data_ton_nest)## # A tibble: 6 x 3
## # Groups: IDX [6]
## IDX train test
## <chr> <list<df[,2]>> <list<df[,2]>>
## 1 AREA0001-A-A09-2006 [71 × 2] [12 × 2]
## 2 AREA0001-A-A10-2006 [71 × 2] [12 × 2]
## 3 AREA0001-A-A11-2006 [71 × 2] [12 × 2]
## 4 AREA0001-A-A12-2006 [71 × 2] [12 × 2]
## 5 AREA0001-A-A13-2006 [71 × 2] [12 × 2]
## 6 AREA0001-A-A14-2006 [71 × 2] [12 × 2]# list fungsi yang akan digunakan untuk membentuk time series
define_function <- list(
ts = function(x) ts(x$value, frequency = 12),
msts6 = function(x) msts(x$value, seasonal.periods = c(12, 6)),
msts18 = function(x) msts(x$value, seasonal.periods = c(12, 18)),
msts24 = function(x) msts(x$value, seasonal.periods = c(12, 24)),
msts30 = function(x) msts(x$value, seasonal.periods = c(12, 30))
)
function_list <- define_function %>%
rep(nrow(harvest_data_ton_nest)) %>%
enframe("func_name", "func")
# replicate data harvest_data_ton_nest
harvest_data_ton_nest_rep <- harvest_data_ton_nest[rep(seq_len(nrow(harvest_data_ton_nest)), each = length(define_function)), ]
# kombinasikan function_list dengan masing-masing GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM
harvest_data_ton_nest <- merge(harvest_data_ton_nest_rep, function_list, all.x=TRUE)
harvest_data_ton_nest <- harvest_data_ton_nest %>%
separate(IDX, c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM"), "-") %>%
mutate(
IDX = paste(GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM, sep = "-")
) %>%
select(-c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM")) %>%
distinct_all() %>%
arrange(IDX)
head(harvest_data_ton_nest)## train
## 1 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 2 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 3 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 4 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 5 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 6 15737.00000000, 15765.00000000, 15796.00000000, 15826.00000000, 15857.00000000, 15887.00000000, 15918.00000000, 15949.00000000, 15979.00000000, 16010.00000000, 16040.00000000, 16071.00000000, 16102.00000000, 16130.00000000, 16161.00000000, 16191.00000000, 16222.00000000, 16252.00000000, 16283.00000000, 16314.00000000, 16344.00000000, 16375.00000000, 16405.00000000, 16436.00000000, 16467.00000000, 16495.00000000, 16526.00000000, 16556.00000000, 16587.00000000, 16617.00000000, 16648.00000000, 16679.00000000, 16709.00000000, 16740.00000000, 16770.00000000, 16801.00000000, 16832.00000000, 16861.00000000, 16892.00000000, 16922.00000000, 16953.00000000, 16983.00000000, 17014.00000000, 17045.00000000, 17075.00000000, 17106.00000000, 17136.00000000, 17167.00000000, 17198.00000000, 17226.00000000, 17257.00000000, 17287.00000000, 17318.00000000, 17348.00000000, 17379.00000000, 17410.00000000, 17440.00000000, 17471.00000000, 17501.00000000, 17532.00000000, 17563.00000000, 17591.00000000, 17622.00000000, 17652.00000000, 17683.00000000, 17713.00000000, 17744.00000000, 17775.00000000, 17805.00000000, 17836.00000000, 17866.00000000, 0.23488283, 1.72235902, 0.29563287, 0.32900949, 0.17982756, -0.82510242, -1.00440739, -0.71277091, 0.24603790, -0.20271147, 0.02458003, 0.58540568, 0.42902553, -0.05336575, 0.64551574, 0.22747611, -0.07263208, -0.48783207, 0.09529714, -0.42448935, -0.05558497, 0.06302577, 0.28546576, -0.35087662, -0.58519483, -0.01276991, 0.19814264, -0.36648074, 0.56626363, -0.38204867, -1.02796498, -0.79636503, 0.45910911, 1.74295221, -0.46310464, 0.72681042, -0.53350246, -1.01944036, -1.37193041, -1.07124244, -1.69796694, -1.54431892, -1.35212948, 0.72981377, 2.14325890, 3.06478442, 0.34647263, 0.28350721, -1.28232231, -1.70685329, -1.92208442, -1.40599098, -1.16707323, 0.26479760, 0.53903811, 1.99072772, 0.72833414, -0.49002263, -0.56234140, -1.23711640, -1.87697371, -0.52543623, -1.11232147, -0.01956313, -0.05845600, 1.41359066, 2.04309552, 2.02403195, -0.09859413, 0.25399828, 0.43687451
## test
## 1 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 2 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 3 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 4 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 5 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 6 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.57650268, -0.38030137, -0.43668820, -0.04633929, -0.38156655, 0.66692531, 1.81218044, 1.68351597, 0.12751799, 0.54989694, -0.02331350, 0.41190683
## func_name func
## 1 ts function (x) , ts(x$value, frequency = 12)
## 2 msts6 function (x) , msts(x$value, seasonal.periods = c(12, 6))
## 3 msts18 function (x) , msts(x$value, seasonal.periods = c(12, 18))
## 4 msts24 function (x) , msts(x$value, seasonal.periods = c(12, 24))
## 5 msts30 function (x) , msts(x$value, seasonal.periods = c(12, 30))
## 6 ts function (x) , ts(x$value, frequency = 12)
## IDX
## 1 AREA0001-A-A09-2006
## 2 AREA0001-A-A09-2006
## 3 AREA0001-A-A09-2006
## 4 AREA0001-A-A09-2006
## 5 AREA0001-A-A09-2006
## 6 AREA0001-A-A10-2006# list model yang akan digunakan untuk pemodelan time series
define_model <- list(
ets = function(x) ets(x),
auto.arima = function(x) auto.arima(x),
stlm = function(x) stlm(x),
tbats = function(x) tbats(x, use.box.cox = FALSE)
)
model_list <- define_model %>%
rep(nrow(harvest_data_ton_nest)) %>%
enframe("model_name", "model")
# replicate data harvest_data_ton_nest
harvest_data_ton_nest_rep <- harvest_data_ton_nest[rep(seq_len(nrow(harvest_data_ton_nest)), each = length(define_model)), ]
# kombinasikan function_list dengan masing-masing GROUP_AREA, SUB_GROUP_AREA, AREA, TAHUN_TANAM
harvest_data_ton_nest <- merge(harvest_data_ton_nest_rep, model_list, all.x=TRUE)
harvest_data_ton_nest <- harvest_data_ton_nest %>%
filter(
!(model_name == "ets" & stringr::str_detect(func_name,"msts")),
!(model_name == "auto.arima" & stringr::str_detect(func_name,"msts"))
) %>%
distinct_all() %>%
arrange(IDX)
head(harvest_data_ton_nest)## train
## 1 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 2 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 3 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 4 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 5 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 6 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## test
## 1 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 2 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 3 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 4 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 5 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 6 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## func_name func
## 1 ts function (x) , ts(x$value, frequency = 12)
## 2 ts function (x) , ts(x$value, frequency = 12)
## 3 ts function (x) , ts(x$value, frequency = 12)
## 4 msts6 function (x) , msts(x$value, seasonal.periods = c(12, 6))
## 5 msts18 function (x) , msts(x$value, seasonal.periods = c(12, 18))
## 6 msts24 function (x) , msts(x$value, seasonal.periods = c(12, 24))
## IDX model_name model
## 1 AREA0001-A-A09-2006 ets function (x) , ets(x)
## 2 AREA0001-A-A09-2006 auto.arima function (x) , auto.arima(x)
## 3 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 4 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 5 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 6 AREA0001-A-A09-2006 stlm function (x) , stlm(x)harvest_data_ton_nest_res <- harvest_data_ton_nest %>%
mutate(
params = map(train, ~ list(x = .x)),
data = invoke_map(func, params),
params = map(data, ~ list(x = .x)),
fitted = invoke_map(model, params)
) %>%
select(-data, -params)harvest_data_ton_nest_res <- harvest_data_ton_nest_res %>%
mutate(
error = map(fitted, ~ forecast(.x, h = 12)) %>%
map2_dbl(test, ~ rmse_vec(truth = .y$value, estimate = .x$mean))
) %>%
arrange(IDX, error)
harvest_data_ton_nest_res %>%
select(IDX, ends_with("_name"), error) %>%
head()## IDX func_name model_name error
## 1 AREA0001-A-A09-2006 ts auto.arima 0.6222030
## 2 AREA0001-A-A09-2006 ts ets 0.7331191
## 3 AREA0001-A-A09-2006 msts6 stlm 0.8112818
## 4 AREA0001-A-A09-2006 ts stlm 0.8584748
## 5 AREA0001-A-A09-2006 msts24 stlm 0.8628837
## 6 AREA0001-A-A09-2006 ts tbats 0.8716747harvest_data_ton_test <- harvest_data_ton_nest_res %>%
mutate(
forecast = map(fitted, ~ forecast(.x, h = 12)) %>%
map2(test, ~ tibble(PERIOD = .y$PERIOD, value = as.vector(.x$mean))),
key = paste(func_name, model_name, sep = "-")
)
head(harvest_data_ton_test)## train
## 1 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 2 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 3 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 4 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 5 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## 6 15737.000000000, 15765.000000000, 15796.000000000, 15826.000000000, 15857.000000000, 15887.000000000, 15918.000000000, 15949.000000000, 15979.000000000, 16010.000000000, 16040.000000000, 16071.000000000, 16102.000000000, 16130.000000000, 16161.000000000, 16191.000000000, 16222.000000000, 16252.000000000, 16283.000000000, 16314.000000000, 16344.000000000, 16375.000000000, 16405.000000000, 16436.000000000, 16467.000000000, 16495.000000000, 16526.000000000, 16556.000000000, 16587.000000000, 16617.000000000, 16648.000000000, 16679.000000000, 16709.000000000, 16740.000000000, 16770.000000000, 16801.000000000, 16832.000000000, 16861.000000000, 16892.000000000, 16922.000000000, 16953.000000000, 16983.000000000, 17014.000000000, 17045.000000000, 17075.000000000, 17106.000000000, 17136.000000000, 17167.000000000, 17198.000000000, 17226.000000000, 17257.000000000, 17287.000000000, 17318.000000000, 17348.000000000, 17379.000000000, 17410.000000000, 17440.000000000, 17471.000000000, 17501.000000000, 17532.000000000, 17563.000000000, 17591.000000000, 17622.000000000, 17652.000000000, 17683.000000000, 17713.000000000, 17744.000000000, 17775.000000000, 17805.000000000, 17836.000000000, 17866.000000000, 0.439943243, 0.646382223, 0.507221066, 0.539435856, 0.220973269, -0.354312964, -0.628715052, -0.031330416, 1.248992781, 0.590738353, -0.048216839, 0.013404362, 0.055296129, -0.594522566, 2.103311086, -0.278838666, -0.131964298, -0.527220212, 0.249462419, -0.546269700, 0.006667538, 0.055296129, -0.021787204, -0.906991725, -1.092712751, -0.577045956, -0.058990408, -0.267973334, 1.098585317, 0.424312383, -0.150133137, -0.093863123, 0.119070270, 1.604319748, -0.829248064, 0.523460539, -0.887695409, -1.177742856, -1.501240623, -1.363301238, -1.496034804, -1.720308445, -0.987570997, 0.509739072, 1.573429116, 2.418286711, 0.995714415, 0.519600574, -1.337074059, -1.726353181, -1.842791288, -1.451177432, -1.570509461, 0.445269713, 0.343070961, 2.328623365, 1.411634811, -0.296708570, 0.544234041, -1.009112538, -2.039824254, -1.141158040, -1.188635139, -0.370627971, -0.516127431, 1.081559422, 1.571781891, 1.511939773, 0.874576708, 0.758716450, 0.401434506
## test
## 1 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 2 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 3 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 4 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 5 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## 6 17897.000000000, 17928.000000000, 17956.000000000, 17987.000000000, 18017.000000000, 18048.000000000, 18078.000000000, 18109.000000000, 18140.000000000, 18170.000000000, 18201.000000000, 18231.000000000, 0.284325530, -0.019519344, -0.017026613, -0.644852275, 0.089642019, -0.338093718, 1.960924338, 1.226254351, 0.006667538, -0.061287557, 0.742556937, -0.201945294
## func_name func
## 1 ts function (x) , ts(x$value, frequency = 12)
## 2 ts function (x) , ts(x$value, frequency = 12)
## 3 msts6 function (x) , msts(x$value, seasonal.periods = c(12, 6))
## 4 ts function (x) , ts(x$value, frequency = 12)
## 5 msts24 function (x) , msts(x$value, seasonal.periods = c(12, 24))
## 6 ts function (x) , ts(x$value, frequency = 12)
## IDX model_name model
## 1 AREA0001-A-A09-2006 auto.arima function (x) , auto.arima(x)
## 2 AREA0001-A-A09-2006 ets function (x) , ets(x)
## 3 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 4 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 5 AREA0001-A-A09-2006 stlm function (x) , stlm(x)
## 6 AREA0001-A-A09-2006 tbats function (x) , tbats(x, use.box.cox = FALSE)
## fitted
## 1 0.4903533, 0.3338692, 0.6792032, 0.01106494, -0.003505102, -0.003505102, 0.0186109, TRUE, TRUE, -86.84433, 179.6887, 1, 0, 0, 1, 12, 0, 0, 0.3636655, 0.4084516, 0.1803961, 0.2755993, -0.04161792, -0.4395041, -0.4328877, 0.259999, 1.193909, -0.0312818, -0.3430081, -0.01226154, -0.05253748, -0.7468243, 2.324752, -1.389733, 0.01785129, -0.3215228, 0.6415226, -0.7467245, -0.1028525, 0.06194699, 0.05991644, -0.8876523, -0.6328424, 0.2066026, -0.5475551, 0.2222373, 1.223315, -0.00760825, -0.5708048, 0.2276163, 0.1992741, 1.524745, -1.634183, 1.225128, -0.9329488, -0.8113385, -0.7409804, -0.7012671, -1.235626, -0.9841157, 0.04644362, 0.9179917, 1.256909, 1.137943, 0.3551838, -0.3773104, -1.280402, -0.799846, -0.748889, -0.3134408, -0.4464071, 1.543906, 0.1092261, 1.853916, -0.1498244, -1.368791, 0.5711554, -1.149988, -1.117528, 0.1261183, -0.3790352, 0.3168704, -0.1853482, 0.8191849, 1.004968, 0.1222507, 0.1832136, 0.7868585, -0.1612928, auto.arima(y = x, x = structure(list(x = structure(c(0.439943243477115, , 0.646382223403823, 0.507221065509904, 0.539435856075594, 0.22097326891895, , -0.354312963516924, -0.628715051639276, -0.0313304162907524, , 1.24899278142289, 0.590738353145232, -0.0482168386147401, 0.0134043616853884, , 0.055296129022589, -0.594522565834199, 2.10331108621846, -0.278838666317569, , -0.13196429768318, -0.527220212034269, 0.249462418991852, -0.546269699543616, , 0.00666753772435798, 0.055296129022589, -0.0217872043781236, , -0.906991724708391, -1.09271275054059, -0.577045956339734, -0.0589904083362626, , -0.267973334267926, 1.09858531658832, 0.42431238279382, -0.150133137323791, , -0.0938631231149501, 0.119070270204471, 1.60431974809528, -0.82924806372074, , 0.523460538841504, -0.887695409464104, -1.1777428558033, -1.50124062286479, , -1.3633012377137, -1.4960348038992, -1.72030844511066, -0.987570996804981, , 0.509739072482273, 1.57342911593998, 2.41828671142089, 0.995714414670864, , 0.519600574048427, -1.33707405927222, 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## 2 -147.1465, 300.2931, 307.0811, 300.6513, 0.8889567, 1.516758, 294.2931, 0.7950185, 0.4749662, 0, 47, -0.03502298, 0.1992599, -0.09831657, 0.01206172, -0.3159902, -0.6400584, -0.4056022, 0.5142437, 1.385734, -0.3742047, -0.7156602, -0.08507588, 0.02445279, -0.6448063, 2.56566, -1.856237, -0.2336198, -0.4431436, 0.6858464, -0.6551463, 0.4186444, 0.1344429, -0.04952502, -0.8953562, -0.3692525, 0.4399769, 0.6082427, -0.08430445, 1.349278, -0.397696, -0.6559658, -0.07819083, 0.1969057, 1.525612, -2.120846, 0.9179745, -1.222988, -0.5407374, -0.4343389, 0.04890796, -0.1227083, -0.2494266, 0.6816096, 1.637027, 1.39925, 1.131678, -1.190599, -0.7201646, -2.004295, -0.8001225, -0.2804484, 0.3341271, -0.05084216, 2.005357, 0.3088624, 2.048863, -0.4970095, -1.810221, 0.4698808, -1.45703, -1.329376, 0.6261688, 0.0808759, 0.8345852, 0.02557505, 1.602929, 0.8187933, 0.1079953, -0.615226, -0.2419702, -0.4068813, 0.4749662, 0.4471223, 0.6055376, 0.5273741, 0.5369634, 0.2857454, -0.2231129, -0.5455741, -0.1367408, 0.9649431, 0.6674434, 0.09848025, 0.03084334, 0.05028376, -0.4623492, 1.577398, 0.1016555, -0.08407657, -0.436384, 0.1088766, -0.4119768, -0.0791468, 0.02773782, -0.01163549, -0.7234603, -1.017023, -0.6672331, -0.1836689, -0.2506925, 0.8220084, 0.5058327, -0.0156723, -0.07783545, 0.07870825, 1.291598, -0.394514, 0.3352928, -0.6370055, -1.066902, -1.412209, -1.373326, -1.470882, -1.669181, -1.127288, 0.1741788, 1.286609, 2.186314, 1.239765, 0.667221, -0.9262307, -1.562343, -1.785305, -1.519667, -1.560088, 0.03420859, 0.2797599, 1.908644, 1.513513, 0.07435322, 0.4479172, -0.7104484, -1.767327, -1.269511, -1.205213, -0.5417025, -0.5213698, 0.7529886, 1.403944, 1.489803, 1.000687, 0.8083159, 0.4749662, 0.4471223, 0.6055376, 0.5273741, 0.5369634, 0.2857454, -0.2231129, -0.5455741, -0.1367408, 0.9649431, 0.6674434, 0.09848025, 0.03084334, 0.05028376, -0.4623492, 1.577398, 0.1016555, -0.08407657, -0.436384, 0.1088766, -0.4119768, -0.0791468, 0.02773782, -0.01163549, -0.7234603, -1.017023, -0.6672331, -0.1836689, -0.2506925, 0.8220084, 0.5058327, -0.0156723, -0.07783545, 0.07870825, 1.291598, -0.394514, 0.3352928, -0.6370055, -1.066902, -1.412209, -1.373326, -1.470882, -1.669181, -1.127288, 0.1741788, 1.286609, 2.186314, 1.239765, 0.667221, -0.9262307, -1.562343, -1.785305, -1.519667, -1.560088, 0.03420859, 0.2797599, 1.908644, 1.513513, 0.07435322, 0.4479172, -0.7104484, -1.767327, -1.269511, -1.205213, -0.5417025, -0.5213698, 0.7529886, 1.403944, 1.489803, 1.000687, 0.8083159, 0.4848376, 0.7950185, 0.4749662, 12, ETS(A,N,N), x, A, N, N, FALSE, ets(y = x), 0.4749662, 0.9147236, 0.4399432, 0.6463822, 0.5072211, 0.5394359, 0.2209733, -0.354313, -0.6287151, -0.03133042, 1.248993, 0.5907384, -0.04821684, 0.01340436, 0.05529613, -0.5945226, 2.103311, -0.2788387, -0.1319643, -0.5272202, 0.2494624, -0.5462697, 0.006667538, 0.05529613, -0.0217872, -0.9069917, -1.092713, -0.577046, -0.05899041, -0.2679733, 1.098585, 0.4243124, -0.1501331, -0.09386312, 0.1190703, 1.60432, -0.8292481, 0.5234605, -0.8876954, -1.177743, -1.501241, -1.363301, -1.496035, -1.720308, -0.987571, 0.5097391, 1.573429, 2.418287, 0.9957144, 0.5196006, -1.337074, -1.726353, -1.842791, -1.451177, -1.570509, 0.4452697, 0.343071, 2.328623, 1.411635, -0.2967086, 0.544234, -1.009113, -2.039824, -1.141158, -1.188635, -0.370628, -0.5161274, 1.081559, 1.571782, 1.51194, 0.8745767, 0.7587164, 0.4014345
## 3 0.4399432, 0.6463822, 0.5072211, 0.5394359, 0.2209733, -0.354313, -0.6287151, -0.03133042, 1.248993, 0.5907384, -0.04821684, 0.01340436, 0.05529613, -0.5945226, 2.103311, -0.2788387, -0.1319643, -0.5272202, 0.2494624, -0.5462697, 0.006667538, 0.05529613, -0.0217872, -0.9069917, -1.092713, -0.577046, -0.05899041, -0.2679733, 1.098585, 0.4243124, -0.1501331, -0.09386312, 0.1190703, 1.60432, -0.8292481, 0.5234605, -0.8876954, -1.177743, -1.501241, -1.363301, -1.496035, -1.720308, -0.987571, 0.5097391, 1.573429, 2.418287, 0.9957144, 0.5196006, -1.337074, -1.726353, -1.842791, -1.451177, -1.570509, 0.4452697, 0.343071, 2.328623, 1.411635, -0.2967086, 0.544234, -1.009113, -2.039824, -1.141158, -1.188635, -0.370628, -0.5161274, 1.081559, 1.571782, 1.51194, 0.8745767, 0.7587164, 0.4014345, 0.7268548, 0.6576999, 0.588545, 0.5193902, 0.4674194, 0.4154487, 0.3634779, 0.3162927, 0.2691075, 0.2219223, 0.2065306, 0.1911388, 0.1757471, 0.1437901, 0.1118331, 0.07987614, 0.0198256, -0.04022495, 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## 4 0.4399432, 0.6463822, 0.5072211, 0.5394359, 0.2209733, -0.354313, -0.6287151, -0.03133042, 1.248993, 0.5907384, -0.04821684, 0.01340436, 0.05529613, -0.5945226, 2.103311, -0.2788387, -0.1319643, -0.5272202, 0.2494624, -0.5462697, 0.006667538, 0.05529613, -0.0217872, -0.9069917, -1.092713, -0.577046, -0.05899041, -0.2679733, 1.098585, 0.4243124, -0.1501331, -0.09386312, 0.1190703, 1.60432, -0.8292481, 0.5234605, -0.8876954, -1.177743, -1.501241, -1.363301, -1.496035, -1.720308, -0.987571, 0.5097391, 1.573429, 2.418287, 0.9957144, 0.5196006, -1.337074, -1.726353, -1.842791, -1.451177, -1.570509, 0.4452697, 0.343071, 2.328623, 1.411635, -0.2967086, 0.544234, -1.009113, -2.039824, -1.141158, -1.188635, -0.370628, -0.5161274, 1.081559, 1.571782, 1.51194, 0.8745767, 0.7587164, 0.4014345, 0.7301439, 0.6607392, 0.5913345, 0.5219298, 0.4699017, 0.4178737, 0.3658456, 0.3182552, 0.2706648, 0.2230744, 0.2070867, 0.1910991, 0.1751114, 0.1435068, 0.1119021, 0.08029749, 0.01990735, -0.04048278, 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1.092035, 1.229562, 0.8448852, 0.9525046, 0.6687736, 0.1609093, -0.3008447, -0.4207495, 0.06700151, -0.22541, -0.2413393, -0.08280595, 0.4443986, 0.2281265, 1.528358, 0.7021979, 0.3745793, -0.08161004, 0.1344445, -0.6331143, -0.8087781, -0.8856392, -0.4818161, -0.7514967, -0.5014724, -0.08773401, 0.08827122, 0.1940784, 0.9855978, 0.7186385, 0.1445486, -0.3918465, -0.6628342, 0.1461496, -0.6281871, 0.09911656, -0.01651227, -0.2603465, -0.8270312, -0.8173521, -1.00432, -1.410963, -1.217771, -0.5736516, 0.1796531, 0.9687641, 0.8140031, 0.6272253, -0.06181909, -0.593135, -1.103556, -0.9549784, -1.077848, -0.1091584, 0.05479118, 0.9840877, 0.6425794, -0.5769956, -0.05237948, -0.6564131, -0.9540885, -0.5326341, -0.6237339, -0.08076946, -0.06954867, 0.6453369, 1.086884, 0.8069728, 0.2248939, -0.06553737, 0.0332353, 0.6320592, 1.086231, 12, ETS(A,N,N), x, A, N, N, FALSE, ets(y = x, model = etsmodel, allow.multiplicative.trend = allow.multiplicative.trend), 1.086231, 0.6411088, 1.095413, 1.309621, 0.620953, 1.015153, 0.5036052, -0.1347339, -0.5696456, -0.4905496, 0.3509361, -0.3956315, -0.2506122, 0.009481099, 0.7513003, 0.1022279, 2.285261, 0.2212654, 0.1838627, -0.3471716, 0.2602164, -1.079934, -0.9110373, -0.9303823, -0.2467385, -0.9084859, -0.3559257, 0.1531156, 0.1907292, 0.2556719, 1.446365, 0.5632334, -0.1896464, -0.7040983, -0.8205844, 0.6170835, -1.078952, 0.522502, -0.0838233, -0.4022897, -1.156915, -0.8117177, -1.11316, -1.647682, -1.105308, -0.1986902, 0.6181745, 1.428129, 0.723912, 0.5184963, -0.4629326, -0.9024301, -1.400688, -0.8684867, -1.149373, 0.4547449, 0.1502311, 1.525059, 0.4437772, -1.286947, 0.2530154, -1.008039, -1.127374, -0.2872928, -0.6767659, 0.2353065, -0.0630167, 1.061493, 1.343922, 0.6440278, -0.1139517, -0.2346062, 0.09073385, function (x, ...) , {, return(ets(x, model = etsmodel, allow.multiplicative.trend = allow.multiplicative.trend, , ...)), }, 0.4399432, 0.6463822, 0.5072211, 0.5394359, 0.2209733, -0.354313, -0.6287151, 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## 5 0.4399432, 0.6463822, 0.5072211, 0.5394359, 0.2209733, -0.354313, -0.6287151, -0.03133042, 1.248993, 0.5907384, -0.04821684, 0.01340436, 0.05529613, -0.5945226, 2.103311, -0.2788387, -0.1319643, -0.5272202, 0.2494624, -0.5462697, 0.006667538, 0.05529613, -0.0217872, -0.9069917, -1.092713, -0.577046, -0.05899041, -0.2679733, 1.098585, 0.4243124, -0.1501331, -0.09386312, 0.1190703, 1.60432, -0.8292481, 0.5234605, -0.8876954, -1.177743, -1.501241, -1.363301, -1.496035, -1.720308, -0.987571, 0.5097391, 1.573429, 2.418287, 0.9957144, 0.5196006, -1.337074, -1.726353, -1.842791, -1.451177, -1.570509, 0.4452697, 0.343071, 2.328623, 1.411635, -0.2967086, 0.544234, -1.009113, -2.039824, -1.141158, -1.188635, -0.370628, -0.5161274, 1.081559, 1.571782, 1.51194, 0.8745767, 0.7587164, 0.4014345, 0.5388806, 0.5069693, 0.475058, 0.4431468, 0.4112355, 0.3793243, 0.347948, 0.3165718, 0.2851955, 0.2538193, 0.2224431, 0.192903, 0.1633629, 0.1338228, 0.1042827, 0.07474263, 0.05492021, 0.03509778, 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0.7949043, 1.308846, -0.3278192, -0.008675811, 1.556573, 2.710871, -0.244688, 1.308846, -0.03389018, -0.2880328, 1.556573, -1.393214, -0.244688, 0.01671877, 0.5781242, -0.2880328, 0.7817998, -1.393214, -0.07308242, -1.025831, 0.5781242, -0.40005, 0.7817998, -0.2452508, -1.966742, -1.025831, -0.6950122, -0.40005, -0.3236785, -0.8959072, -1.966742, 0.6966077, -0.6950122, -0.3993967, -0.8649567, -0.8959072, 0.1090532, 0.6966077, -0.3968783, 0.02876874, -0.8649567, 0.3705026, 0.1090532, -0.4073174, -0.1192491, 0.02876874, -0.5485293, 0.3705026, -0.2769812, 1.488877, -0.1192491, 0.7541385, -0.5485293, -0.1151406, 1.848763, 1.488877, 0.5894333, 0.7541385, 0.02729385, 1.62708, 1.848763, 0.3257871, 0.5894333, 0.1014649, 0.8472829, 1.62708, -0.005619441, 0.3257871, 0.1590007, 0.6572515, 0.8472829, 0.5962042, -0.005619441, 0.1802233, 0.2424338, 0.6572515, 0.7679153, 0.5962042, 0.4399432, 0.6463822, 0.5072211, 0.5394359, 0.2209733, -0.354313, -0.6287151, -0.03133042, 1.248993, 0.5907384, -0.04821684, 0.01340436, 0.05529613, -0.5945226, 2.103311, -0.2788387, -0.1319643, -0.5272202, 0.2494624, -0.5462697, 0.006667538, 0.05529613, -0.0217872, -0.9069917, -1.092713, -0.577046, -0.05899041, -0.2679733, 1.098585, 0.4243124, -0.1501331, -0.09386312, 0.1190703, 1.60432, -0.8292481, 0.5234605, -0.8876954, -1.177743, -1.501241, -1.363301, -1.496035, -1.720308, -0.987571, 0.5097391, 1.573429, 2.418287, 0.9957144, 0.5196006, -1.337074, -1.726353, -1.842791, -1.451177, -1.570509, 0.4452697, 0.343071, 2.328623, 1.411635, -0.2967086, 0.544234, -1.009113, -2.039824, -1.141158, -1.188635, -0.370628, -0.5161274, 1.081559, 1.571782, 1.51194, 0.8745767, 0.7587164, 0.4014345, tbats(y = x, use.box.cox = FALSE), x, TBATS
## error
## 1 0.6222030
## 2 0.7331191
## 3 0.8112818
## 4 0.8584748
## 5 0.8628837
## 6 0.8716747
## forecast
## 1 17897.0000000000, 17928.0000000000, 17956.0000000000, 17987.0000000000, 18017.0000000000, 18048.0000000000, 18078.0000000000, 18109.0000000000, 18140.0000000000, 18170.0000000000, 18201.0000000000, 18231.0000000000, -0.1871000917, -0.4648525228, -0.1858349969, -0.2176728443, -0.0009434551, -0.0623446096, 0.2429294520, 0.4546487608, 0.2637542291, 0.1905020728, 0.3561208813, 0.1207744003
## 2 17897.0000000, 17928.0000000, 17956.0000000, 17987.0000000, 18017.0000000, 18048.0000000, 18078.0000000, 18109.0000000, 18140.0000000, 18170.0000000, 18201.0000000, 18231.0000000, 0.4848376, 0.4848376, 0.4848376, 0.4848376, 0.4848376, 0.4848376, 0.4848376, 0.4848376, 0.4848376, 0.4848376, 0.4848376, 0.4848376
## 3 17897.0000000, 17928.0000000, 17956.0000000, 17987.0000000, 18017.0000000, 18048.0000000, 18078.0000000, 18109.0000000, 18140.0000000, 18170.0000000, 18201.0000000, 18231.0000000, 0.1972944, -0.7969874, -0.5652584, -0.5871418, -0.6121325, -0.4218421, 0.2054246, 0.3372222, 1.1175243, 0.9432292, 0.9980429, 0.3478088
## 4 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, 0.03216211, -0.87921461, -0.82062992, -0.47863396, -0.57269921, -0.41987543, 0.05330133, 0.26109486, 0.90114727, 1.02176372, 1.02655794, 0.34393596
## 5 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, -0.15143412, -1.01870326, -0.84939509, -0.89420202, -0.62660825, -0.30344207, 0.22166240, -0.09838313, 0.89012404, 0.86756414, 0.51072591, -0.17678070
## 6 17897.00000000, 17928.00000000, 17956.00000000, 17987.00000000, 18017.00000000, 18048.00000000, 18078.00000000, 18109.00000000, 18140.00000000, 18170.00000000, 18201.00000000, 18231.00000000, -0.74685344, -1.24169490, -1.37717671, -1.13213988, -0.59317859, 0.07470851, 0.67808834, 1.05069775, 1.09881724, 0.82416352, 0.31893223, -0.26431225
## key
## 1 ts-auto.arima
## 2 ts-ets
## 3 msts6-stlm
## 4 ts-stlm
## 5 msts24-stlm
## 6 ts-tbatsharvest_data_ton_test_x <- harvest_data_ton_test %>%
select(IDX, key, actual = test, forecast) %>%
spread(key, forecast) %>%
gather(key, value, -IDX)
harvest_data_ton_test_x <- harvest_data_ton_test_x %>%
unnest(value) %>%
mutate(value = ton_revert(value, ton_recipe, IDX))
harvest_data_ton_test_x %>%
separate(IDX, c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM"), "-") %>%
filter(
TAHUN_TANAM >= 2006 & TAHUN_TANAM <= 2011 & GROUP_AREA == "AREA0001" & ( AREA == "A09" | AREA == "A10" | AREA == "A11")
) %>%
ggplot(aes(x = PERIOD, y = value, colour = key)) +
geom_line() +
labs(x = NULL, y = NULL, colour = NULL) +
facet_wrap(~ AREA, scale = "free", ncol = 1) +
theme_light()+
theme(legend.position = "bottom")+
theme(legend.title=element_text(size=8))+
theme(legend.text=element_text(size=6))+
tidyquant::scale_colour_tq()
ton_model_selected <- harvest_data_ton_nest_res %>%
group_by(IDX) %>%
filter(error == min(error)) %>%
ungroup() %>%
distinct_all()
head(ton_model_selected)## # A tibble: 6 x 9
## train test func_name func IDX model_name model fitted error
## <list<df[> <list<df[> <chr> <list> <chr> <chr> <lis> <list> <dbl>
## 1 [71 × 2] [12 × 2] ts <fn> AREA000… auto.arima <fn> <fr_AR… 0.622
## 2 [71 × 2] [12 × 2] ts <fn> AREA000… ets <fn> <ets> 0.716
## 3 [71 × 2] [12 × 2] ts <fn> AREA000… ets <fn> <ets> 0.486
## 4 [71 × 2] [12 × 2] ts <fn> AREA000… auto.arima <fn> <fr_AR… 0.642
## 5 [71 × 2] [12 × 2] msts24 <fn> AREA000… stlm <fn> <stlm> 0.541
## 6 [71 × 2] [12 × 2] ts <fn> AREA000… ets <fn> <ets> 0.770harvest_data_ton_test_x %>%
filter(
paste(IDX, key, sep = "-") %in%
paste(ton_model_selected$IDX,
ton_model_selected$func_name,
ton_model_selected$model_name,
sep = "-")
| key == "actual"
) %>%
separate(IDX, c("GROUP_AREA", "SUB_GROUP_AREA", "AREA", "TAHUN_TANAM"), "-") %>%
filter(
TAHUN_TANAM >= 2006 & TAHUN_TANAM <= 2011 & GROUP_AREA == "AREA0001" & ( AREA == "A09" | AREA == "A10" | AREA == "A11")
) %>%
ggplot(aes(x = PERIOD, y = value, colour = key)) +
geom_line() +
labs(x = NULL, y = NULL, colour = NULL) +
facet_wrap(~ AREA, scale = "free", ncol = 1) +
theme_light()+
theme(legend.position = "bottom")+
theme(legend.title=element_text(size=8))+
theme(legend.text=element_text(size=6))+
tidyquant::scale_colour_tq()
ton_mape <- harvest_data_ton_test_x %>%
filter(
paste(IDX, key, sep = "-") %in%
paste(ton_model_selected$IDX,
ton_model_selected$func_name,
ton_model_selected$model_name,
sep = "-")
| key == "actual"
) %>%
mutate(
value = as.numeric(value),
key = if_else(key == "actual", "actual", "forecast")
) %>%
distinct_all() %>%
# filter(key != "actual") %>%
spread(key, value)
# MAPE per IDX
ton_mape_by_IDX <- ton_mape %>%
group_by(IDX) %>%
nest() %>%
mutate(
tmp = map(data, ~invoke_map_dfc(list(mape = mae_vec),
truth = .x$actual, estimate = .x$forecast))
) %>%
select(-data) %>%
unnest() %>%
ungroup() %>%
add_row(IDX = "all",
mape = MLmetrics::MAPE(ton_mape$forecast, ton_mape$actual)*100) %>%
mutate(
mape = round(mape,4)
) %>%
arrange(IDX)
ton_mape_by_IDX## # A tibble: 66 x 2
## IDX mape
## <chr> <dbl>
## 1 all 22.3
## 2 AREA0001-A-A09-2006 2.36
## 3 AREA0001-A-A10-2006 12.4
## 4 AREA0001-A-A11-2006 13.9
## 5 AREA0001-A-A12-2006 16.7
## 6 AREA0001-A-A13-2006 11.3
## 7 AREA0001-A-A14-2006 17.3
## 8 AREA0001-A-A15-2006 17.5
## 9 AREA0001-A-A16-2006 9.60
## 10 AREA0001-A-A17-2006 11.5
## # … with 56 more rows