UAS METODE PERAMALAN
Safira Khoirulanisa Salsabila
2024-12-20
Persiapan Data
Packages
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readr)## Warning: package 'readr' was built under R version 4.4.2library(ggplot2)
library(forecast)## Warning: package 'forecast' was built under R version 4.4.2## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoolibrary(tseries)## Warning: package 'tseries' was built under R version 4.4.2library(car)## Warning: package 'car' was built under R version 4.4.2## Loading required package: carData## Warning: package 'carData' was built under R version 4.4.2##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recodedata <- read.csv("saham bank mandiri.csv", sep = ";", stringsAsFactors = FALSE)
head(data)## Date Open High Low Close Volume
## 1 01/04/2021 15:00 3175.0 3262.5 3112.5 3250.0 30529000
## 2 01/05/2021 15:00 3275.0 3275.0 3200.0 3212.5 34964100
## 3 01/06/2021 15:00 3212.5 3262.5 3137.5 3212.5 43554500
## 4 01/07/2021 16:00 3225.0 3262.5 3212.5 3237.5 40057100
## 5 01/08/2021 16:00 3237.5 3287.5 3225.0 3275.0 46817800
## 6 01/11/2021 15:00 3287.5 3425.0 3275.0 3425.0 106381500dim(data)## [1] 732 6fugsi dim() digunakan untuk melihat jumlah observasi dan variabel pada dataset. pada dataset data, terdapat 732 observasi dan 6 variabel. nama setiap variabel dapat dilihat dengan menggunakan fungsi names().
names(data)## [1] "Date" "Open" "High" "Low" "Close" "Volume"Memeriksa Data yang Hilang (Missing Data)
colSums(is.na(data))## Date Open High Low Close Volume
## 0 0 0 0 0 0Data Saham Bank Mandiri dinyatakan tidak terdapat missing data berdasarkan hasil di atas.
Memisahkan Data Close
data.new <- select(data, 1,5)
data.new <- as.data.frame(data.new)
head(data.new)## Date Close
## 1 01/04/2021 15:00 3250.0
## 2 01/05/2021 15:00 3212.5
## 3 01/06/2021 15:00 3212.5
## 4 01/07/2021 16:00 3237.5
## 5 01/08/2021 16:00 3275.0
## 6 01/11/2021 15:00 3425.0Memeriksa Struktur Data
Data akan diubah menjadi data time series dengan fungsi ts()
rmarkdown::paged_table(data.new)data_ts <- ts(data.new$Close)
data_ts## Time Series:
## Start = 1
## End = 732
## Frequency = 1
## [1] 3250.0 3212.5 3212.5 3237.5 3275.0 3425.0 3475.0 3462.5 3425.0 3362.5
## [11] 3462.5 3487.5 3687.5 3625.0 3600.0 3650.0 3687.5 3650.0 3525.0 3287.5
## [21] 3350.0 3237.5 3250.0 3287.5 3275.0 3287.5 3250.0 3250.0 3250.0 3250.0
## [31] 3237.5 3175.0 3125.0 3187.5 3187.5 3162.5 3187.5 3137.5 3075.0 3287.5
## [41] 3300.0 3300.0 3275.0 3237.5 3212.5 3212.5 3250.0 3362.5 3325.0 3275.0
## [51] 3262.5 3400.0 3387.5 3312.5 3337.5 3212.5 3200.0 3212.5 3187.5 3162.5
## [61] 3075.0 3100.0 3112.5 3137.5 3162.5 3137.5 3237.5 3150.0 3150.0 3162.5
## [71] 3150.0 3150.0 3112.5 3100.0 3062.5 3062.5 3075.0 2987.5 3025.0 3100.0
## [81] 3112.5 3087.5 3025.0 3050.0 3025.0 3037.5 2975.0 2987.5 2950.0 2962.5
## [91] 2950.0 2837.5 2825.0 2912.5 2875.0 2950.0 2875.0 2900.0 3000.0 3050.0
## [101] 3125.0 3062.5 3062.5 3000.0 3075.0 3100.0 3137.5 3137.5 3162.5 3137.5
## [111] 3100.0 3100.0 3012.5 3087.5 3012.5 2987.5 2950.0 2850.0 2887.5 2950.0
## [121] 2987.5 2975.0 2925.0 2887.5 2875.0 2950.0 2887.5 2900.0 2887.5 2850.0
## [131] 2937.5 2937.5 2875.0 2962.5 3025.0 2987.5 2900.0 2912.5 2950.0 2875.0
## [141] 2850.0 2837.5 2887.5 2912.5 3025.0 2987.5 2900.0 2900.0 2962.5 3000.0
## [151] 2987.5 3025.0 2925.0 2950.0 2987.5 2912.5 2975.0 2912.5 2900.0 2975.0
## [161] 3050.0 3000.0 3025.0 3062.5 3100.0 3125.0 3125.0 3175.0 3100.0 3075.0
## [171] 3050.0 3062.5 3075.0 3025.0 3012.5 2987.5 3037.5 3037.5 2987.5 2950.0
## [181] 2975.0 3000.0 3075.0 3050.0 3237.5 3212.5 3300.0 3350.0 3450.0 3450.0
## [191] 3500.0 3550.0 3587.5 3575.0 3587.5 3600.0 3587.5 3600.0 3562.5 3587.5
## [201] 3575.0 3512.5 3587.5 3587.5 3512.5 3562.5 3587.5 3550.0 3500.0 3437.5
## [211] 3575.0 3612.5 3550.0 3525.0 3612.5 3612.5 3587.5 3625.0 3675.0 3575.0
## [221] 3575.0 3675.0 3550.0 3587.5 3500.0 3537.5 3600.0 3575.0 3587.5 3650.0
## [231] 3575.0 3675.0 3600.0 3562.5 3587.5 3637.5 3625.0 3575.0 3537.5 3575.0
## [241] 3525.0 3537.5 3525.0 3500.0 3525.0 3550.0 3512.5 3525.0 3587.5 3512.5
## [251] 3512.5 3525.0 3525.0 3525.0 3575.0 3562.5 3587.5 3600.0 3625.0 3512.5
## [261] 3587.5 3650.0 3637.5 3612.5 3737.5 3775.0 3825.0 3737.5 3737.5 3737.5
## [271] 3737.5 3750.0 3812.5 3837.5 3862.5 3862.5 3900.0 3950.0 3925.0 3925.0
## [281] 3925.0 3925.0 3937.5 3937.5 3850.0 3850.0 3825.0 3812.5 3825.0 3775.0
## [291] 3775.0 3812.5 3837.5 3825.0 3875.0 3887.5 3975.0 3975.0 3962.5 3825.0
## [301] 3837.5 3887.5 3962.5 3925.0 3937.5 3962.5 3937.5 3950.0 3937.5 3912.5
## [311] 3912.5 3837.5 3850.0 3850.0 3837.5 3850.0 3862.5 3837.5 3812.5 3787.5
## [321] 3925.0 4150.0 4137.5 4112.5 4175.0 4112.5 4475.0 4162.5 4050.0 4000.0
## [331] 3937.5 3900.0 3925.0 3950.0 3950.0 4000.0 3937.5 3962.5 3925.0 4050.0
## [341] 4100.0 4250.0 4100.0 4100.0 4075.0 4150.0 4200.0 4212.5 4087.5 4050.0
## [351] 4100.0 4150.0 4187.5 4037.5 4125.0 4125.0 4075.0 4075.0 4150.0 4112.5
## [361] 4062.5 3975.0 3962.5 3800.0 3700.0 3825.0 3762.5 3712.5 3737.5 3737.5
## [371] 3712.5 3637.5 3637.5 3587.5 3675.0 3712.5 3950.0 3950.0 3925.0 3887.5
## [381] 4000.0 4050.0 4025.0 4137.5 3987.5 4087.5 4125.0 4125.0 4125.0 4175.0
## [391] 4237.5 4300.0 4262.5 4237.5 4225.0 4262.5 4300.0 4250.0 4262.5 4312.5
## [401] 4312.5 4287.5 4250.0 4275.0 4325.0 4425.0 4462.5 4425.0 4475.0 4412.5
## [411] 4437.5 4475.0 4537.5 4587.5 4725.0 4650.0 4675.0 4550.0 4612.5 4600.0
## [421] 4637.5 4600.0 4600.0 4625.0 4675.0 4650.0 4625.0 4712.5 4637.5 4612.5
## [431] 4637.5 4662.5 4712.5 4650.0 4650.0 4712.5 4737.5 4700.0 4725.0 4750.0
## [441] 4737.5 4950.0 5175.0 5125.0 5112.5 5100.0 5100.0 5100.0 5275.0 5200.0
## [451] 5037.5 5087.5 5075.0 5125.0 5075.0 5075.0 5050.0 5212.5 5125.0 5125.0
## [461] 5000.0 4950.0 5087.5 5050.0 5062.5 5100.0 5112.5 5087.5 5062.5 5162.5
## [471] 5262.5 5200.0 5262.5 5437.5 5450.0 5187.5 5025.0 4962.5 4975.0 4950.0
## [481] 4975.0 4950.0 5050.0 5000.0 5025.0 5000.0 4975.0 4962.5 5025.0 4975.0
## [491] 4987.5 4987.5 4962.5 4937.5 4975.0 5012.5 4912.5 4900.0 4862.5 4637.5
## [501] 4475.0 4600.0 4587.5 4650.0 4875.0 4775.0 4875.0 4987.5 4950.0 4850.0
## [511] 4912.5 5012.5 4975.0 4975.0 4850.0 4862.5 4962.5 4937.5 5087.5 5100.0
## [521] 5125.0 5150.0 5175.0 5187.5 5112.5 5087.5 5150.0 5137.5 5100.0 5000.0
## [531] 5037.5 5062.5 5075.0 5000.0 5050.0 5112.5 5025.0 5062.5 5087.5 5137.5
## [541] 5225.0 5187.5 5175.0 4962.5 5025.0 4925.0 5050.0 5000.0 5250.0 5450.0
## [551] 5100.0 5062.5 5175.0 5112.5 5162.5 5262.5 5200.0 5225.0 5175.0 5100.0
## [561] 5100.0 5150.0 5125.0 5225.0 5125.0 5175.0 5200.0 5200.0 5175.0 5250.0
## [571] 5225.0 5175.0 5175.0 5125.0 5050.0 5075.0 5100.0 5000.0 5050.0 4990.0
## [581] 5000.0 5075.0 5225.0 5125.0 5075.0 5175.0 5125.0 5150.0 5150.0 5050.0
## [591] 5075.0 5050.0 5025.0 5025.0 5125.0 5100.0 5100.0 5050.0 5125.0 5150.0
## [601] 5125.0 5100.0 5100.0 5075.0 5075.0 5125.0 5200.0 5350.0 5250.0 5275.0
## [611] 5300.0 5150.0 5175.0 5275.0 5300.0 5350.0 5400.0 5375.0 5350.0 5525.0
## [621] 5550.0 5500.0 5550.0 5575.0 5625.0 5700.0 5725.0 5650.0 5700.0 5800.0
## [631] 5775.0 5875.0 5900.0 5900.0 5975.0 5925.0 5950.0 5800.0 5800.0 5775.0
## [641] 5825.0 5950.0 5850.0 5950.0 5900.0 6000.0 5950.0 5975.0 6025.0 6075.0
## [651] 6100.0 6100.0 6050.0 5875.0 5900.0 5925.0 5875.0 5850.0 5825.0 5925.0
## [661] 5900.0 6000.0 6075.0 6000.0 6000.0 6000.0 5925.0 6000.0 6025.0 6050.0
## [671] 6075.0 6125.0 6125.0 6025.0 5900.0 6000.0 6050.0 6075.0 6075.0 6025.0
## [681] 6000.0 5850.0 5725.0 5750.0 5675.0 5750.0 5850.0 5700.0 5700.0 5725.0
## [691] 5675.0 5650.0 5900.0 5825.0 5925.0 5875.0 5850.0 5875.0 5775.0 5825.0
## [701] 5825.0 5925.0 5925.0 5900.0 5875.0 5875.0 5875.0 5900.0 5875.0 5900.0
## [711] 5900.0 5850.0 5850.0 5900.0 6000.0 5975.0 5800.0 5750.0 5700.0 5775.0
## [721] 5800.0 5725.0 5950.0 5900.0 5925.0 5975.0 5925.0 5975.0 5975.0 6000.0
## [731] 6125.0 6050.0Eksplorasi Data
Summary Data Saham Bank Mandiri
sumarry() untuk melihat penjelasan yang lebih lengkap dari dua data di atas seperti minimal, kuartal pertama, median, mean, kuartal ketiga, dan maksimum dari penutupan harga saham mingguan saham bank mandiri.
summary(data.new)## Date Close
## Length:732 Min. :2825
## Class :character 1st Qu.:3484
## Mode :character Median :4100
## Mean :4306
## 3rd Qu.:5116
## Max. :6125Membangun Model ARIMA
Uji Stasioneritas Mean dengan Augmented Dickey Fuller
Uji Augmented Dickey Fuller digunakan untuk menguji apakah data stasioner atau tidak. Perlu diingat, data yang baik untuk dilakukan forecasting adalah data yang stasioner. Data stasioner adalah data time series yang memiliki nilai rata-rata dan varians konstan.
adf_test_mean <- adf.test(data_ts)
print(adf_test_mean)##
## Augmented Dickey-Fuller Test
##
## data: data_ts
## Dickey-Fuller = -3.3894, Lag order = 9, p-value = 0.05527
## alternative hypothesis: stationaryHail dari uji Augmented Dickey Fuller pada data time series saham bank mandiri menghasilkan p value sebesar 0.05527 yang lebih dari nilai alpha. Dengan tingkat kepercayaan 95%, terbukti bahwa data tidak stasioner. Data akan dilakukan differencing atau perbedaan untuk mengatasi data yang tidak stasioner.
Differencing Data
# Melakukan differencing jika diperlukan
data_diff <- diff(data_ts)
# Plot data yang telah di-difference
plot(data_diff, main="Data Setelah Differencing", ylab="Harga Penutupan (Differenced)", xlab="Date")
adf_test2 <- adf.test(data_diff)## Warning in adf.test(data_diff): p-value smaller than printed p-valueprint(adf_test2)##
## Augmented Dickey-Fuller Test
##
## data: data_diff
## Dickey-Fuller = -9.2431, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationaryHasil dari Augmented Dickey Fuller menghasilkan p value sebesar 0.01 yang kurang dari nilai alpha.
Uji Stasioneritas
fit <- auto.arima(data_ts)
residuals <- residuals(fit)
residuals## Time Series:
## Start = 1
## End = 732
## Frequency = 1
## [1] 3.2461601 -40.8925914 -8.3373934 16.7865280 35.0206256
## [6] 151.8417568 67.6572838 4.5688874 -35.1529076 -70.2606652
## [11] 84.6393584 25.6783267 206.3428631 -38.9483606 -16.4465108
## [16] 40.9101175 37.3012182 -33.3709403 -129.8206842 -260.0020767
## [21] 16.0445207 -135.9951771 -6.6485954 21.5392855 -14.2548452
## [26] 8.7082589 -41.4577527 -8.1881096 -8.2877984 -5.5334343
## [31] -17.7046143 -68.9652169 -63.7511629 45.1243724 -3.6166526
## [36] -25.5284834 17.7366028 -53.7902327 -71.4467358 195.4443914
## [41] 26.6412473 15.6748016 -24.7043610 -43.0583349 -36.1624611
## [46] -11.8447076 29.2050010 111.2512830 -25.2934597 -47.6826927
## [51] -24.2774283 126.7253722 -2.8469453 -68.6486784 12.5230806
## [56] -133.0140174 -31.5765563 -6.2637741 -32.2305976 -33.3037999
## [61] -98.0944434 6.3865158 1.2863344 21.8503196 23.9430583
## [66] -24.0907936 95.2043141 -81.6893223 -5.9194433 1.1443762
## [71] -16.6903672 -5.7857832 -43.4342223 -22.1352761 -47.6591623
## [76] -11.5119269 3.2897126 -91.8928448 22.6885005 66.2972513
## [81] 18.6626821 -21.0410427 -67.3615176 11.1676721 -33.0728724
## [86] 5.5426397 -68.4100252 0.7499326 -46.9351005 2.9797305
## [91] -19.8764276 -118.5231082 -32.4972064 69.8285503 -35.4941322
## [96] 72.6242937 -72.9011700 18.2784877 92.3367737 58.9829694
## [101] 86.0591398 -50.9005417 -2.9116454 -70.9272292 62.2388260
## [106] 22.8812723 41.6377934 3.1606141 25.0110643 -25.5142373
## [111] -42.3810918 -11.1469185 -96.2267113 58.4575837 -79.6853178
## [116] -33.7298762 -52.0927284 -113.0187147 15.4975088 51.1603985
## [121] 41.2120931 -7.0399133 -51.2727889 -48.1989351 -26.5009447
## [126] 63.9103050 -60.7198485 6.5447540 -20.5863828 -43.3135478
## [131] 76.6487371 1.9411560 -59.7268221 76.5131949 63.0597072
## [136] -27.2577268 -89.4305125 -4.5503299 25.6682183 -76.0750176
## [141] -36.0146348 -27.0721450 39.8535909 23.7883253 114.8873456
## [146] -25.2988662 -84.8809946 -16.3306187 49.6047349 38.3749991
## [151] -7.5165108 35.9329589 -100.0653600 11.9027340 26.7975772
## [156] -74.5686258 51.7636469 -66.2035997 -20.1364739 63.1920665
## [161] 77.2214884 -39.1321805 22.7937949 33.1974958 39.6203226
## [166] 28.7715479 2.9778501 48.9187922 -72.6033422 -33.6563117
## [171] -38.9948172 1.0904703 5.5526247 -53.0679216 -22.3715003
## [176] -35.9891724 39.8967325 -1.9478975 -50.7589514 -47.7126151
## [181] 11.1013051 18.5529259 74.3556589 -18.1949378 187.6180165
## [186] -7.3884333 98.3590630 57.5856896 111.3889137 14.5834051
## [191] 57.2093252 54.3764148 45.0742860 -6.2997847 11.6389602
## [196] 9.5624488 -14.1999863 7.7190292 -41.5742638 16.7153703
## [201] -17.7495228 -67.1205953 61.4708680 -1.8961751 -73.9586057
## [206] 36.9522548 19.5343445 -35.8745668 -56.6045251 -76.2492619
## [211] 119.6225801 41.9619747 -51.2551042 -31.6219775 75.5293979
## [216] 2.7763812 -22.2176775 31.1734340 48.1296287 -95.3553014
## [221] -11.5065724 86.8241554 -119.1685277 26.2965766 -98.0292846
## [226] 23.8506100 53.4292023 -20.3156163 10.6183073 58.2720317
## [231] -70.8232404 92.3393334 -73.4260463 -42.6463318 9.8364718
## [236] 43.8194126 -10.1571264 -51.4374942 -48.4783080 23.4511664
## [241] -54.9992329 3.8804927 -20.4367621 -31.0167845 15.6661933
## [246] 20.4999834 -37.5264415 5.7735987 56.2479019 -71.4738417
## [251] -7.9086126 1.7503965 -4.2816068 -4.2166673 45.2896845
## [256] -11.1457788 23.5637653 10.6189660 24.4209392 -112.4662579
## [261] 59.4276829 56.5830006 -4.4711781 -24.6802178 117.7693641
## [266] 46.0234248 61.5878754 -79.9732684 -8.5048168 -11.5293506
## [271] -5.9565074 6.9740513 59.0200510 28.9652694 29.6147606
## [276] 2.1950219 36.3930990 50.7985277 -19.5946063 -2.0122035
## [281] -5.7138741 -4.7048462 7.6108343 -3.2979295 -91.1089443
## [286] -15.2633580 -38.2825323 -22.2928708 2.7499426 -55.3554063
## [291] -10.3845274 27.7877086 23.6992416 -11.1269060 46.7707037
## [296] 13.4608580 89.1985539 8.1982652 -7.9174171 -141.6254801
## [301] -9.3302694 33.2428799 74.4545737 -29.4612760 11.2473424
## [306] 20.0885050 -25.4442561 7.2181337 -17.5705956 -30.3884469
## [311] -9.0185653 -82.4689613 -2.1816053 -10.9629772 -17.8613764
## [316] 5.5640986 7.8575260 -27.4138139 -31.5399999 -34.9780263
## [321] 126.7580735 233.7672398 22.8135101 -6.6069107 59.7502572
## [326] -59.5746962 356.3389781 -277.6794586 -120.6920183 -91.7004893
## [331] -87.5977619 -59.6848096 6.5726529 17.0031013 -1.2106097
## [336] 47.4275469 -60.6341523 17.6846048 -44.2158381 117.2207609
## [341] 56.8317797 162.8651022 -129.1790598 -6.1053746 -40.3274291
## [346] 65.7183918 50.8516923 20.3504700 -122.1188609 -54.5921931
## [351] 29.3253128 45.2113051 41.6338397 -144.9829971 69.3792768
## [356] -7.4029697 -48.9749579 -10.4478913 65.8105250 -34.1523734
## [361] -52.5456093 -100.6092764 -33.0213462 -178.7459600 -128.4610239
## [366] 90.5755517 -65.9348358 -54.3761431 9.0237852 -7.2554553
## [371] -28.9758151 -82.9879076 -16.4046815 -62.7469085 74.6179997
## [376] 37.5766144 244.4657730 29.2069562 -4.9347643 -39.5134346
## [381] 103.4153606 55.5331540 -13.4418377 111.6345524 -141.2928846
## [386] 88.1518817 32.7014844 7.4945219 -0.2016103 46.7603456
## [391] 64.3680924 70.4282847 -27.3658147 -26.3310012 -21.8364998
## [396] 28.7996379 35.3707943 -47.1142641 5.8367293 42.9583451
## [401] 1.9048846 -25.0328142 -44.2435837 13.6651622 44.1551894
## [406] 102.7023476 49.9034222 -26.6901579 47.0449309 -62.7997543
## [411] 17.3877377 30.5677455 63.8489194 56.5182333 145.8885466
## [416] -56.2829123 26.4047650 -130.2867372 44.9115816 -21.6755179
## [421] 34.7435775 -38.8883338 -5.7086468 17.2293280 47.7958166
## [426] -21.5556908 -27.5019157 78.5033364 -71.5170280 -31.0632289
## [431] 11.4128865 19.9755544 49.5556857 -58.6119338 -6.8909265
## [436] 52.9444950 27.0687215 -33.6225780 19.2976257 20.7277514
## [441] -12.1965899 208.8926872 245.7141946 -6.3943162 3.3117705
## [446] -16.4645859 -5.5779180 -5.8900064 169.9770868 -58.5242991
## [451] -159.3664580 21.7901351 -26.9235256 44.6785848 -50.6087799
## [456] -6.3169711 -33.8197490 153.9972784 -75.3026065 -0.2487878
## [461] -135.1304933 -70.3976496 113.8088725 -33.2629756 14.0545880
## [466] 32.6159533 13.8224309 -24.4342430 -30.6793103 90.3750832
## [471] 104.6717492 -46.0120864 61.7344052 174.8913753 35.2005308
## [476] -247.4867216 -193.7012335 -110.6254647 -20.9851530 -40.6057417
## [481] 14.4469884 -30.4466142 93.6367646 -44.9097001 23.4517041
## [486] -29.7024165 -30.5233575 -22.5439090 53.3644385 -49.1815180
## [491] 7.0799925 -7.0613948 -29.1137015 -32.9886464 27.2033167
## [496] 34.2480527 -97.3844211 -25.4092527 -52.5461420 -237.3823000
## [501] -199.7611906 76.9730922 -23.5286810 62.1828687 226.8158897
## [506] -70.9070786 106.3444785 115.7810871 -18.3244539 -96.4530688
## [511] 45.3330255 93.6895302 -26.1017224 0.7580208 -130.9159281
## [516] -7.2981023 84.3821609 -19.1174472 150.8388718 25.5334149
## [521] 36.8300033 27.7927246 27.6260195 14.3541137 -74.7841119
## [526] -36.7974733 47.9393889 -13.5307629 -39.0077971 -109.7375614
## [531] 16.9871812 14.1155682 11.8020108 -76.2199395 37.8145719
## [536] 56.9518413 -81.2232917 28.4566782 17.8904605 50.7177796
## [541] 91.3569085 -25.9394408 -11.9160885 -219.9536315 30.7505495
## [546] -118.3650280 109.2320700 -50.3119031 249.0873632 222.4641541
## [551] -305.8974920 -60.2781277 75.8471386 -62.0676564 44.8724117
## [556] 96.4925031 -50.7969662 22.9686264 -55.2511015 -83.6904598
## [561] -18.6756112 36.9167286 -25.8728997 96.0650367 -94.2321701
## [566] 42.6169980 18.5417934 1.9750103 -27.0546103 68.0147979
## [571] -22.7635357 -50.9684188 -11.9691939 -59.5414368 -87.1208424
## [576] 5.5477741 14.5961989 -101.5904178 34.9418043 -68.0094633
## [581] 0.7436700 65.5974421 154.2521909 -79.5042875 -50.7418387
## [586] 83.3402126 -47.8575728 22.2346923 -5.0965007 -102.6130142
## [591] 8.1790846 -36.3700167 -32.6094199 -10.8536743 92.1217508
## [596] -18.4658569 1.5622127 -55.1825353 64.5378638 24.4719173
## [601] -20.4763474 -29.3093321 -9.1281215 -32.3926353 -8.5618191
## [606] 42.4202804 75.6417069 158.9471730 -78.0944242 24.8211312
## [611] 17.7056856 -149.6071521 4.3235556 84.2502639 31.8328185
## [616] 57.0637626 55.7929887 -17.2644423 -26.3116935 166.5058514
## [621] 39.3526701 -35.1758421 45.1290021 23.7601189 52.8225746
## [626] 79.6026130 35.2969641 -67.8987362 40.7867556 95.5075668
## [631] -13.7572598 102.4200535 32.5531136 8.6628444 74.9290441
## [636] -43.9471786 22.0138519 -154.7983575 -20.9535525 -44.2751081
## [641] 39.0005184 122.2533899 -85.6323453 95.8472544 -49.2281716
## [646] 98.1069767 -45.9243821 23.6992403 45.2435030 53.6699397
## [651] 31.4927918 4.4792138 -50.6711670 -184.6674192 -5.6534435
## [656] 5.1137021 -53.6824377 -34.9832105 -37.5838341 88.6528227
## [661] -21.1131917 100.9497409 81.7614717 -60.4367558 -4.4361693
## [666] -9.4068092 -80.3583955 60.5444245 21.8896361 28.8755266
## [671] 26.5161660 51.8083189 4.7076585 -98.9538526 -140.5577935
## [676] 70.7301946 43.1303521 32.3222074 3.7042986 -50.6970389
## [681] -34.7350244 -162.3052049 -151.5913243 -10.8882462 -92.7765207
## [686] 58.9013664 95.6559008 -137.2327694 -12.6799145 8.1981927
## [691] -53.8892156 -34.7520249 237.4272662 -52.6690432 109.4590495
## [696] -44.8211180 -25.2217389 14.3477536 -104.1794900 34.6042653
## [701] -8.2660715 98.0276608 7.4721482 -19.7720955 -30.6367681
## [706] -9.2320252 -7.5157363 19.4742516 -27.0796948 19.4668919
## [711] -3.7074530 -52.6731659 -10.5932152 40.4852110 100.2359492
## [716] -13.2036231 -172.1189937 -76.0019829 -77.4528578 55.3622588
## [721] 21.4969976 -71.6034562 214.1857831 -33.5780055 34.8629575
## [726] 47.6365159 -45.1088486 44.6021407 -2.1303518 24.6100000
## [731] 123.9967012 -61.6156352Uji Stasioneritas Varians dengan Uji Lavene
data_date <- as.POSIXct(data$Date, format="%d/%m/%Y %H:%M")
data_date## [1] "2021-04-01 15:00:00 WIB" "2021-05-01 15:00:00 WIB"
## [3] "2021-06-01 15:00:00 WIB" "2021-07-01 16:00:00 WIB"
## [5] "2021-08-01 16:00:00 WIB" "2021-11-01 15:00:00 WIB"
## [7] "2021-12-01 15:00:00 WIB" NA
## [9] NA NA
## [11] NA NA
## [13] NA NA
## [15] NA NA
## [17] NA NA
## [19] NA NA
## [21] "2021-01-02 15:00:00 WIB" "2021-02-02 15:00:00 WIB"
## [23] "2021-03-02 15:00:00 WIB" "2021-04-02 15:00:00 WIB"
## [25] "2021-05-02 15:00:00 WIB" "2021-08-02 15:00:00 WIB"
## [27] "2021-09-02 15:00:00 WIB" "2021-10-02 15:00:00 WIB"
## [29] "2021-11-02 15:00:00 WIB" NA
## [31] NA NA
## [33] NA NA
## [35] NA NA
## [37] NA NA
## [39] NA "2021-01-03 15:00:00 WIB"
## [41] "2021-02-03 15:00:00 WIB" "2021-03-03 15:00:00 WIB"
## [43] "2021-04-03 15:00:00 WIB" "2021-05-03 15:00:00 WIB"
## [45] "2021-08-03 16:00:00 WIB" "2021-09-03 15:00:00 WIB"
## [47] "2021-10-03 15:00:00 WIB" "2021-12-03 15:00:00 WIB"
## [49] NA NA
## [51] NA NA
## [53] NA NA
## [55] NA NA
## [57] NA NA
## [59] NA NA
## [61] NA "2021-01-04 15:00:00 WIB"
## [63] "2021-05-04 15:00:00 WIB" "2021-06-04 15:00:00 WIB"
## [65] "2021-07-04 15:00:00 WIB" "2021-08-04 15:00:00 WIB"
## [67] "2021-09-04 15:00:00 WIB" "2021-12-04 15:00:00 WIB"
## [69] NA NA
## [71] NA NA
## [73] NA NA
## [75] NA NA
## [77] NA NA
## [79] NA NA
## [81] NA NA
## [83] "2021-03-05 16:00:00 WIB" "2021-04-05 16:00:00 WIB"
## [85] "2021-05-05 15:00:00 WIB" "2021-06-05 15:00:00 WIB"
## [87] "2021-07-05 15:00:00 WIB" "2021-10-05 16:00:00 WIB"
## [89] "2021-11-05 16:00:00 WIB" NA
## [91] NA NA
## [93] NA NA
## [95] NA NA
## [97] NA NA
## [99] NA "2021-02-06 15:00:00 WIB"
## [101] "2021-03-06 15:00:00 WIB" "2021-04-06 15:00:00 WIB"
## [103] "2021-07-06 15:00:00 WIB" "2021-08-06 15:00:00 WIB"
## [105] "2021-09-06 15:00:00 WIB" "2021-10-06 15:00:00 WIB"
## [107] "2021-11-06 15:00:00 WIB" NA
## [109] NA NA
## [111] NA NA
## [113] NA NA
## [115] NA NA
## [117] NA NA
## [119] NA NA
## [121] "2021-01-07 15:00:00 WIB" "2021-02-07 15:00:00 WIB"
## [123] "2021-05-07 15:00:00 WIB" "2021-06-07 15:00:00 WIB"
## [125] "2021-07-07 15:00:00 WIB" "2021-08-07 15:00:00 WIB"
## [127] "2021-09-07 15:00:00 WIB" "2021-12-07 15:00:00 WIB"
## [129] NA NA
## [131] NA NA
## [133] NA NA
## [135] NA NA
## [137] NA NA
## [139] NA NA
## [141] NA "2021-02-08 15:00:00 WIB"
## [143] "2021-03-08 15:00:00 WIB" "2021-04-08 15:00:00 WIB"
## [145] "2021-05-08 15:00:00 WIB" "2021-06-08 15:00:00 WIB"
## [147] "2021-09-08 15:00:00 WIB" "2021-10-08 15:00:00 WIB"
## [149] "2021-12-08 15:00:00 WIB" NA
## [151] NA NA
## [153] NA NA
## [155] NA NA
## [157] NA NA
## [159] NA NA
## [161] NA "2021-01-09 15:00:00 WIB"
## [163] "2021-02-09 15:00:00 WIB" "2021-03-09 15:00:00 WIB"
## [165] "2021-06-09 15:00:00 WIB" "2021-07-09 15:00:00 WIB"
## [167] "2021-08-09 15:00:00 WIB" "2021-09-09 15:00:00 WIB"
## [169] "2021-10-09 15:00:00 WIB" NA
## [171] NA NA
## [173] NA NA
## [175] NA NA
## [177] NA NA
## [179] NA NA
## [181] NA NA
## [183] NA "2021-01-10 15:00:00 WIB"
## [185] "2021-04-10 15:00:00 WIB" "2021-05-10 15:00:00 WIB"
## [187] "2021-06-10 15:00:00 WIB" "2021-07-10 15:00:00 WIB"
## [189] "2021-08-10 15:00:00 WIB" "2021-11-10 15:00:00 WIB"
## [191] "2021-12-10 16:00:00 WIB" NA
## [193] NA NA
## [195] NA NA
## [197] NA NA
## [199] NA NA
## [201] NA NA
## [203] NA "2021-01-11 15:00:00 WIB"
## [205] "2021-02-11 15:00:00 WIB" "2021-03-11 15:00:00 WIB"
## [207] "2021-04-11 16:00:00 WIB" "2021-05-11 15:00:00 WIB"
## [209] "2021-08-11 15:00:00 WIB" "2021-09-11 15:00:00 WIB"
## [211] "2021-10-11 15:00:00 WIB" "2021-11-11 15:00:00 WIB"
## [213] "2021-12-11 15:00:00 WIB" NA
## [215] NA NA
## [217] NA NA
## [219] NA NA
## [221] NA NA
## [223] NA NA
## [225] NA "2021-01-12 16:00:00 WIB"
## [227] "2021-02-12 16:00:00 WIB" "2021-03-12 16:00:00 WIB"
## [229] "2021-06-12 16:00:00 WIB" "2021-07-12 16:00:00 WIB"
## [231] "2021-08-12 16:00:00 WIB" "2021-09-12 16:00:00 WIB"
## [233] "2021-10-12 16:00:00 WIB" NA
## [235] NA NA
## [237] NA NA
## [239] NA NA
## [241] NA NA
## [243] NA NA
## [245] NA NA
## [247] NA "2022-03-01 16:00:00 WIB"
## [249] "2022-04-01 16:00:00 WIB" "2022-05-01 16:00:00 WIB"
## [251] "2022-06-01 16:00:00 WIB" "2022-07-01 16:00:00 WIB"
## [253] "2022-10-01 16:00:00 WIB" "2022-11-01 16:00:00 WIB"
## [255] "2022-12-01 16:00:00 WIB" NA
## [257] NA NA
## [259] NA NA
## [261] NA NA
## [263] NA NA
## [265] NA NA
## [267] NA NA
## [269] "2022-02-02 16:00:00 WIB" "2022-03-02 16:00:00 WIB"
## [271] "2022-04-02 16:00:00 WIB" "2022-07-02 16:00:00 WIB"
## [273] "2022-08-02 16:00:00 WIB" "2022-09-02 16:00:00 WIB"
## [275] "2022-10-02 16:00:00 WIB" "2022-11-02 16:00:00 WIB"
## [277] NA NA
## [279] NA NA
## [281] NA NA
## [283] NA NA
## [285] NA NA
## [287] "2022-01-03 16:00:00 WIB" "2022-02-03 16:00:00 WIB"
## [289] "2022-04-03 16:00:00 WIB" "2022-07-03 16:00:00 WIB"
## [291] "2022-08-03 16:00:00 WIB" "2022-09-03 16:00:00 WIB"
## [293] "2022-10-03 16:00:00 WIB" "2022-11-03 16:00:00 WIB"
## [295] NA NA
## [297] NA NA
## [299] NA NA
## [301] NA NA
## [303] NA NA
## [305] NA NA
## [307] NA NA
## [309] "2022-01-04 16:00:00 WIB" "2022-04-04 16:00:00 WIB"
## [311] "2022-05-04 16:00:00 WIB" "2022-06-04 16:00:00 WIB"
## [313] "2022-07-04 16:00:00 WIB" "2022-08-04 16:00:00 WIB"
## [315] "2022-11-04 16:00:00 WIB" "2022-12-04 16:00:00 WIB"
## [317] NA NA
## [319] NA NA
## [321] NA NA
## [323] NA NA
## [325] NA NA
## [327] NA "2022-09-05 16:00:00 WIB"
## [329] "2022-10-05 16:00:00 WIB" "2022-11-05 16:00:00 WIB"
## [331] "2022-12-05 16:00:00 WIB" NA
## [333] NA NA
## [335] NA NA
## [337] NA NA
## [339] NA NA
## [341] NA NA
## [343] "2022-02-06 16:00:00 WIB" "2022-03-06 16:00:00 WIB"
## [345] "2022-06-06 16:00:00 WIB" "2022-07-06 16:00:00 WIB"
## [347] "2022-08-06 16:00:00 WIB" "2022-09-06 16:00:00 WIB"
## [349] "2022-10-06 16:00:00 WIB" NA
## [351] NA NA
## [353] NA NA
## [355] NA NA
## [357] NA NA
## [359] NA NA
## [361] NA NA
## [363] NA "2022-01-07 16:00:00 WIB"
## [365] "2022-04-07 16:00:00 WIB" "2022-05-07 16:00:00 WIB"
## [367] "2022-06-07 16:00:00 WIB" "2022-07-07 16:00:00 WIB"
## [369] "2022-08-07 16:00:00 WIB" "2022-11-07 16:00:00 WIB"
## [371] "2022-12-07 16:00:00 WIB" NA
## [373] NA NA
## [375] NA NA
## [377] NA NA
## [379] NA NA
## [381] NA NA
## [383] NA NA
## [385] "2022-01-08 16:00:00 WIB" "2022-02-08 16:00:00 WIB"
## [387] "2022-03-08 16:00:00 WIB" "2022-04-08 16:00:00 WIB"
## [389] "2022-05-08 16:00:00 WIB" "2022-08-08 16:00:00 WIB"
## [391] "2022-09-08 16:00:00 WIB" "2022-10-08 16:00:00 WIB"
## [393] "2022-11-08 16:00:00 WIB" "2022-12-08 16:00:00 WIB"
## [395] NA NA
## [397] NA NA
## [399] NA NA
## [401] NA NA
## [403] NA NA
## [405] NA NA
## [407] "2022-01-09 15:00:00 WIB" "2022-02-09 15:00:00 WIB"
## [409] "2022-05-09 16:00:00 WIB" "2022-06-09 16:00:00 WIB"
## [411] "2022-07-09 16:00:00 WIB" "2022-08-09 16:00:00 WIB"
## [413] "2022-09-09 16:00:00 WIB" "2022-12-09 16:00:00 WIB"
## [415] NA NA
## [417] NA NA
## [419] NA NA
## [421] NA NA
## [423] NA NA
## [425] NA NA
## [427] NA NA
## [429] "2022-03-10 16:00:00 WIB" "2022-04-10 16:00:00 WIB"
## [431] "2022-05-10 16:00:00 WIB" "2022-06-10 16:00:00 WIB"
## [433] "2022-07-10 16:00:00 WIB" "2022-10-10 16:00:00 WIB"
## [435] "2022-11-10 16:00:00 WIB" "2022-12-10 16:00:00 WIB"
## [437] NA NA
## [439] NA NA
## [441] NA NA
## [443] NA NA
## [445] NA NA
## [447] NA NA
## [449] NA "2022-01-11 15:00:00 WIB"
## [451] "2022-02-11 15:00:00 WIB" "2022-03-11 16:00:00 WIB"
## [453] "2022-04-11 16:00:00 WIB" "2022-07-11 16:00:00 WIB"
## [455] "2022-08-11 16:00:00 WIB" "2022-09-11 16:00:00 WIB"
## [457] "2022-10-11 16:00:00 WIB" "2022-11-11 16:00:00 WIB"
## [459] NA NA
## [461] NA NA
## [463] NA NA
## [465] NA NA
## [467] NA NA
## [469] NA NA
## [471] NA "2022-01-12 16:00:00 WIB"
## [473] "2022-02-12 16:00:00 WIB" "2022-05-12 16:00:00 WIB"
## [475] "2022-06-12 16:00:00 WIB" "2022-07-12 16:00:00 WIB"
## [477] "2022-08-12 16:00:00 WIB" "2022-09-12 16:00:00 WIB"
## [479] "2022-12-12 16:00:00 WIB" NA
## [481] NA NA
## [483] NA NA
## [485] NA NA
## [487] NA NA
## [489] NA NA
## [491] NA NA
## [493] NA "2023-02-01 16:00:00 WIB"
## [495] "2023-03-01 16:00:00 WIB" "2023-04-01 16:00:00 WIB"
## [497] "2023-05-01 16:00:00 WIB" "2023-06-01 16:00:00 WIB"
## [499] "2023-09-01 16:00:00 WIB" "2023-10-01 16:00:00 WIB"
## [501] "2023-11-01 16:00:00 WIB" "2023-12-01 16:00:00 WIB"
## [503] NA NA
## [505] NA NA
## [507] NA NA
## [509] NA NA
## [511] NA NA
## [513] NA NA
## [515] "2023-01-02 16:00:00 WIB" "2023-02-02 16:00:00 WIB"
## [517] "2023-03-02 16:00:00 WIB" "2023-06-02 16:00:00 WIB"
## [519] "2023-07-02 16:00:00 WIB" "2023-08-02 16:00:00 WIB"
## [521] "2023-09-02 16:00:00 WIB" "2023-10-02 16:00:00 WIB"
## [523] NA NA
## [525] NA NA
## [527] NA NA
## [529] NA NA
## [531] NA NA
## [533] NA NA
## [535] "2023-01-03 16:00:00 WIB" "2023-02-03 16:00:00 WIB"
## [537] "2023-03-03 16:00:00 WIB" "2023-06-03 16:00:00 WIB"
## [539] "2023-07-03 16:00:00 WIB" "2023-08-03 16:00:00 WIB"
## [541] "2023-09-03 16:00:00 WIB" "2023-10-03 16:00:00 WIB"
## [543] NA NA
## [545] NA NA
## [547] NA NA
## [549] NA NA
## [551] NA NA
## [553] NA NA
## [555] NA "2023-03-04 16:00:00 WIB"
## [557] "2023-04-04 16:00:00 WIB" "2023-05-04 16:00:00 WIB"
## [559] "2023-06-04 16:00:00 WIB" "2023-10-04 16:00:00 WIB"
## [561] "2023-11-04 16:00:00 WIB" "2023-12-04 16:00:00 WIB"
## [563] NA NA
## [565] NA NA
## [567] NA NA
## [569] NA "2023-02-05 16:00:00 WIB"
## [571] "2023-03-05 16:00:00 WIB" "2023-04-05 16:00:00 WIB"
## [573] "2023-05-05 16:00:00 WIB" "2023-08-05 16:00:00 WIB"
## [575] "2023-09-05 16:00:00 WIB" "2023-10-05 16:00:00 WIB"
## [577] "2023-11-05 16:00:00 WIB" "2023-12-05 16:00:00 WIB"
## [579] NA NA
## [581] NA NA
## [583] NA NA
## [585] NA NA
## [587] NA NA
## [589] NA NA
## [591] "2023-05-06 16:00:00 WIB" "2023-06-06 16:00:00 WIB"
## [593] "2023-07-06 16:00:00 WIB" "2023-08-06 16:00:00 WIB"
## [595] "2023-09-06 16:00:00 WIB" "2023-12-06 16:00:00 WIB"
## [597] NA NA
## [599] NA NA
## [601] NA NA
## [603] NA NA
## [605] NA NA
## [607] NA "2023-03-07 16:00:00 WIB"
## [609] "2023-04-07 16:00:00 WIB" "2023-05-07 16:00:00 WIB"
## [611] "2023-06-07 16:00:00 WIB" "2023-07-07 16:00:00 WIB"
## [613] "2023-10-07 16:00:00 WIB" "2023-11-07 16:00:00 WIB"
## [615] "2023-12-07 16:00:00 WIB" NA
## [617] NA NA
## [619] NA NA
## [621] NA NA
## [623] NA NA
## [625] NA NA
## [627] NA "2023-01-08 16:00:00 WIB"
## [629] "2023-02-08 16:00:00 WIB" "2023-03-08 16:00:00 WIB"
## [631] "2023-04-08 16:00:00 WIB" "2023-07-08 16:00:00 WIB"
## [633] "2023-08-08 16:00:00 WIB" "2023-09-08 16:00:00 WIB"
## [635] "2023-10-08 16:00:00 WIB" "2023-11-08 16:00:00 WIB"
## [637] NA NA
## [639] NA NA
## [641] NA NA
## [643] NA NA
## [645] NA NA
## [647] NA NA
## [649] NA "2023-01-09 16:00:00 WIB"
## [651] "2023-04-09 16:00:00 WIB" "2023-05-09 16:00:00 WIB"
## [653] "2023-06-09 16:00:00 WIB" "2023-07-09 16:00:00 WIB"
## [655] "2023-08-09 16:00:00 WIB" "2023-11-09 16:00:00 WIB"
## [657] "2023-12-09 16:00:00 WIB" NA
## [659] NA NA
## [661] NA NA
## [663] NA NA
## [665] NA NA
## [667] NA NA
## [669] NA "2023-02-10 16:00:00 WIB"
## [671] "2023-03-10 16:00:00 WIB" "2023-04-10 16:00:00 WIB"
## [673] "2023-05-10 16:00:00 WIB" "2023-06-10 16:00:00 WIB"
## [675] "2023-09-10 16:00:00 WIB" "2023-10-10 16:00:00 WIB"
## [677] "2023-11-10 16:00:00 WIB" "2023-12-10 16:00:00 WIB"
## [679] NA NA
## [681] NA NA
## [683] NA NA
## [685] NA NA
## [687] NA NA
## [689] NA NA
## [691] NA "2023-01-11 16:00:00 WIB"
## [693] "2023-02-11 16:00:00 WIB" "2023-03-11 16:00:00 WIB"
## [695] "2023-06-11 16:00:00 WIB" "2023-07-11 16:00:00 WIB"
## [697] "2023-08-11 16:00:00 WIB" "2023-09-11 16:00:00 WIB"
## [699] "2023-10-11 16:00:00 WIB" NA
## [701] NA NA
## [703] NA NA
## [705] NA NA
## [707] NA NA
## [709] NA NA
## [711] NA NA
## [713] NA "2023-01-12 16:00:00 WIB"
## [715] "2023-04-12 16:00:00 WIB" "2023-05-12 16:00:00 WIB"
## [717] "2023-06-12 16:00:00 WIB" "2023-07-12 16:00:00 WIB"
## [719] "2023-08-12 16:00:00 WIB" "2023-11-12 16:00:00 WIB"
## [721] "2023-12-12 16:00:00 WIB" NA
## [723] NA NA
## [725] NA NA
## [727] NA NA
## [729] NA NA
## [731] NA NA# Mengatur DATE sebagai indeks dan membuat data frame
data_month <- format(data_date, "%Y-%m")
data_month## [1] "2021-04" "2021-05" "2021-06" "2021-07" "2021-08" "2021-11" "2021-12"
## [8] NA NA NA NA NA NA NA
## [15] NA NA NA NA NA NA "2021-01"
## [22] "2021-02" "2021-03" "2021-04" "2021-05" "2021-08" "2021-09" "2021-10"
## [29] "2021-11" NA NA NA NA NA NA
## [36] NA NA NA NA "2021-01" "2021-02" "2021-03"
## [43] "2021-04" "2021-05" "2021-08" "2021-09" "2021-10" "2021-12" NA
## [50] NA NA NA NA NA NA NA
## [57] NA NA NA NA NA "2021-01" "2021-05"
## [64] "2021-06" "2021-07" "2021-08" "2021-09" "2021-12" NA NA
## [71] NA NA NA NA NA NA NA
## [78] NA NA NA NA NA "2021-03" "2021-04"
## [85] "2021-05" "2021-06" "2021-07" "2021-10" "2021-11" NA NA
## [92] NA NA NA NA NA NA NA
## [99] NA "2021-02" "2021-03" "2021-04" "2021-07" "2021-08" "2021-09"
## [106] "2021-10" "2021-11" NA NA NA NA NA
## [113] NA NA NA NA NA NA NA
## [120] NA "2021-01" "2021-02" "2021-05" "2021-06" "2021-07" "2021-08"
## [127] "2021-09" "2021-12" NA NA NA NA NA
## [134] NA NA NA NA NA NA NA
## [141] NA "2021-02" "2021-03" "2021-04" "2021-05" "2021-06" "2021-09"
## [148] "2021-10" "2021-12" NA NA NA NA NA
## [155] NA NA NA NA NA NA NA
## [162] "2021-01" "2021-02" "2021-03" "2021-06" "2021-07" "2021-08" "2021-09"
## [169] "2021-10" NA NA NA NA NA NA
## [176] NA NA NA NA NA NA NA
## [183] NA "2021-01" "2021-04" "2021-05" "2021-06" "2021-07" "2021-08"
## [190] "2021-11" "2021-12" NA NA NA NA NA
## [197] NA NA NA NA NA NA NA
## [204] "2021-01" "2021-02" "2021-03" "2021-04" "2021-05" "2021-08" "2021-09"
## [211] "2021-10" "2021-11" "2021-12" NA NA NA NA
## [218] NA NA NA NA NA NA NA
## [225] NA "2021-01" "2021-02" "2021-03" "2021-06" "2021-07" "2021-08"
## [232] "2021-09" "2021-10" NA NA NA NA NA
## [239] NA NA NA NA NA NA NA
## [246] NA NA "2022-03" "2022-04" "2022-05" "2022-06" "2022-07"
## [253] "2022-10" "2022-11" "2022-12" NA NA NA NA
## [260] NA NA NA NA NA NA NA
## [267] NA NA "2022-02" "2022-03" "2022-04" "2022-07" "2022-08"
## [274] "2022-09" "2022-10" "2022-11" NA NA NA NA
## [281] NA NA NA NA NA NA "2022-01"
## [288] "2022-02" "2022-04" "2022-07" "2022-08" "2022-09" "2022-10" "2022-11"
## [295] NA NA NA NA NA NA NA
## [302] NA NA NA NA NA NA NA
## [309] "2022-01" "2022-04" "2022-05" "2022-06" "2022-07" "2022-08" "2022-11"
## [316] "2022-12" NA NA NA NA NA NA
## [323] NA NA NA NA NA "2022-09" "2022-10"
## [330] "2022-11" "2022-12" NA NA NA NA NA
## [337] NA NA NA NA NA NA "2022-02"
## [344] "2022-03" "2022-06" "2022-07" "2022-08" "2022-09" "2022-10" NA
## [351] NA NA NA NA NA NA NA
## [358] NA NA NA NA NA NA "2022-01"
## [365] "2022-04" "2022-05" "2022-06" "2022-07" "2022-08" "2022-11" "2022-12"
## [372] NA NA NA NA NA NA NA
## [379] NA NA NA NA NA NA "2022-01"
## [386] "2022-02" "2022-03" "2022-04" "2022-05" "2022-08" "2022-09" "2022-10"
## [393] "2022-11" "2022-12" NA NA NA NA NA
## [400] NA NA NA NA NA NA NA
## [407] "2022-01" "2022-02" "2022-05" "2022-06" "2022-07" "2022-08" "2022-09"
## [414] "2022-12" NA NA NA NA NA NA
## [421] NA NA NA NA NA NA NA
## [428] NA "2022-03" "2022-04" "2022-05" "2022-06" "2022-07" "2022-10"
## [435] "2022-11" "2022-12" NA NA NA NA NA
## [442] NA NA NA NA NA NA NA
## [449] NA "2022-01" "2022-02" "2022-03" "2022-04" "2022-07" "2022-08"
## [456] "2022-09" "2022-10" "2022-11" NA NA NA NA
## [463] NA NA NA NA NA NA NA
## [470] NA NA "2022-01" "2022-02" "2022-05" "2022-06" "2022-07"
## [477] "2022-08" "2022-09" "2022-12" NA NA NA NA
## [484] NA NA NA NA NA NA NA
## [491] NA NA NA "2023-02" "2023-03" "2023-04" "2023-05"
## [498] "2023-06" "2023-09" "2023-10" "2023-11" "2023-12" NA NA
## [505] NA NA NA NA NA NA NA
## [512] NA NA NA "2023-01" "2023-02" "2023-03" "2023-06"
## [519] "2023-07" "2023-08" "2023-09" "2023-10" NA NA NA
## [526] NA NA NA NA NA NA NA
## [533] NA NA "2023-01" "2023-02" "2023-03" "2023-06" "2023-07"
## [540] "2023-08" "2023-09" "2023-10" NA NA NA NA
## [547] NA NA NA NA NA NA NA
## [554] NA NA "2023-03" "2023-04" "2023-05" "2023-06" "2023-10"
## [561] "2023-11" "2023-12" NA NA NA NA NA
## [568] NA NA "2023-02" "2023-03" "2023-04" "2023-05" "2023-08"
## [575] "2023-09" "2023-10" "2023-11" "2023-12" NA NA NA
## [582] NA NA NA NA NA NA NA
## [589] NA NA "2023-05" "2023-06" "2023-07" "2023-08" "2023-09"
## [596] "2023-12" NA NA NA NA NA NA
## [603] NA NA NA NA NA "2023-03" "2023-04"
## [610] "2023-05" "2023-06" "2023-07" "2023-10" "2023-11" "2023-12" NA
## [617] NA NA NA NA NA NA NA
## [624] NA NA NA NA "2023-01" "2023-02" "2023-03"
## [631] "2023-04" "2023-07" "2023-08" "2023-09" "2023-10" "2023-11" NA
## [638] NA NA NA NA NA NA NA
## [645] NA NA NA NA NA "2023-01" "2023-04"
## [652] "2023-05" "2023-06" "2023-07" "2023-08" "2023-11" "2023-12" NA
## [659] NA NA NA NA NA NA NA
## [666] NA NA NA NA "2023-02" "2023-03" "2023-04"
## [673] "2023-05" "2023-06" "2023-09" "2023-10" "2023-11" "2023-12" NA
## [680] NA NA NA NA NA NA NA
## [687] NA NA NA NA NA "2023-01" "2023-02"
## [694] "2023-03" "2023-06" "2023-07" "2023-08" "2023-09" "2023-10" NA
## [701] NA NA NA NA NA NA NA
## [708] NA NA NA NA NA NA "2023-01"
## [715] "2023-04" "2023-05" "2023-06" "2023-07" "2023-08" "2023-11" "2023-12"
## [722] NA NA NA NA NA NA NA
## [729] NA NA NA NA# Melakukan uji Levene untuk homogenitas varians
levene_test <- leveneTest(Close ~ data_month, data=data)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.print(levene_test)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 35 1.2814 0.1436
## 252ACF dan PACF
Pada bagian ini, penulis akan melakukan autokorelasi yaitu menganalisis korelasi antara harga hari ini dan harga kemarin. Autocorrelation Function (ACF) dan Partial Autocorrelation (PACF) adalah alat yang dibutuhkan oleh penulis untuk menganalisis autokorelasi antar time series yang akan digambarkan dengan correlogram
acf(data_ts)
pacf(data_ts, lag.max = 50)
arima <- auto.arima(data_ts)
arima## Series: data_ts
## ARIMA(0,1,2) with drift
##
## Coefficients:
## ma1 ma2 drift
## -0.1224 -0.0832 3.8382
## s.e. 0.0370 0.0389 2.0883
##
## sigma^2 = 5066: log likelihood = -4153.61
## AIC=8315.23 AICc=8315.28 BIC=8333.61summary(arima)## Series: data_ts
## ARIMA(0,1,2) with drift
##
## Coefficients:
## ma1 ma2 drift
## -0.1224 -0.0832 3.8382
## s.e. 0.0370 0.0389 2.0883
##
## sigma^2 = 5066: log likelihood = -4153.61
## AIC=8315.23 AICc=8315.28 BIC=8333.61
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.0003535128 70.98417 51.33651 -0.02816251 1.222019 0.9863842
## ACF1
## Training set 0.001654371Forecasting
Prediksi dapat dipanggil dengan menggunakan fungsi forecast dari data *fit**
forecasting <- forecast(arima, h=1)
forecasting## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 733 6051.068 5959.849 6142.288 5911.56 6190.576plot(forecasting)