Komparasi Visualisasi Prediksi Data Inflow dan Outflow Uang Kartal di Kepulauan Sumatera dan Sekitarnya
Shafira Halmahera
13/2/2022
0.0.1 Dosen Pengampu : Prof. Dr. Suhartono, M.Kom
0.0.2 Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang
0.0.3 Jurusan : Teknik Informatika
0.0.4 Fakultas : Sains dan Teknologi
1 Pengertian Inflow-Outflow Uang Kartal
Inflows adalah uang yang masuk ke Bank Indonesia melalui kegiatan penyetoran, dan Outflows adalah uang yang keluar dari Bank Indonesia melalui kegiatan penarikan. Setiap daerah memiliki prediksi data inflow-outflow uang kartal yang berbeda-beda. Berikut komparasi visualisasi prediksi data inflow-outflow uang kartal di Sumatera dan Sekitarnya menggunakan bahasa pemrograman R pada Rstudio.
1.1 Data Inflow-OutFlow Pertahun untuk Daerah Sumatra dan Sekitarnya
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2datainflow <- read_excel(path = "C:/Users/shafira halmahera/Documents/LINEAR ALGEBRA/Data Inflow Sumatra.xlsx")
datainflowtahun <- datainflow[c(1:12),c(1:12)]
datainflowtahun## # A tibble: 12 x 12
## Tahun Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau `Kep. Riau`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2012 65911. 2620. 25981. 11192. 4447. 2236.
## 2 2013 98369. 36337. 18120. 14056. 8933. 3378.
## 3 2014 86024. 4567. 30503. 14103. 6358. 2563.
## 4 2015 86549. 4710. 30254. 13309. 7156. 3218.
## 5 2016 97764. 5775. 34427. 14078. 8211. 4317.
## 6 2017 103748. 5514. 35617. 15312. 8553. 4412.
## 7 2018 117495. 5799. 41769. 15058. 10730. 5134.
## 8 2019 133762. 7509. 47112. 14750. 10915. 6077.
## 9 2020 109345. 6641. 36609. 10696. 9148. 6175.
## 10 2021 89270. 3702. 31840. 10748. 7769. 5009.
## 11 NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA
## # ... with 5 more variables: Jambi <dbl>, `Sumatera Selatan` <dbl>,
## # Bengkulu <dbl>, Lampung <dbl>, `Kep. Bangka Belitung` <dbl>library(readxl)
dataoutflow <- read_excel(path = "C:/Users/shafira halmahera/Documents/LINEAR ALGEBRA/Data Outflow Sumatra.xlsx")
dataoutflowtahun <- dataoutflow[c(1:12),c(1:12)]
dataoutflowtahun## # A tibble: 12 x 12
## Tahun Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau `Kep. Riau`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 80092. 6338. 22176. 5300. 12434. 5819.
## 2 2012 85235. 6378. 22495. 6434. 13014. 6966.
## 3 2013 103288. 23278. 19235. 6511. 15460. 8747.
## 4 2014 102338. 8630. 26391. 7060. 15158. 10122.
## 5 2015 109186. 9637. 27877. 7471. 15789. 9803.
## 6 2016 121992. 11311. 31959. 9198. 17645. 10068.
## 7 2017 133606. 11760. 35243. 10754. 18128. 10749.
## 8 2018 135676. 11450. 36908. 8447. 17926. 12597.
## 9 2019 153484. 13087. 44051. 9465. 19277. 12644.
## 10 2020 140589. 12874. 39758. 8763. 19139. 8461.
## 11 2021 86627. 5770. 23453. 5941. 12631. 5128.
## 12 NA NA NA NA NA NA NA
## # ... with 5 more variables: Jambi <dbl>, `Sumatera Selatan` <dbl>,
## # Bengkulu <dbl>, Lampung <dbl>, `Kep. Bangka Belitung` <dbl>2 Data Inflow-Outflow Perbulan untuk Daerah Sumatra dan Sekitarnya
library(readxl)
datainflowperbulan <- read_excel(path = 'C:/Users/shafira halmahera/Documents/LINEAR ALGEBRA/DataPerbulanInflow.xlsx')
datainflowperbulan## # A tibble: 128 x 12
## Bulan Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 4164. 124. 2068. 545. 94.2
## 2 2011-02-01 00:00:00 3338. 115. 1826. 450. 96.4
## 3 2011-03-01 00:00:00 4878. 154. 2028. 849. 288.
## 4 2011-04-01 00:00:00 3157. 122. 1429. 539. 160.
## 5 2011-05-01 00:00:00 3821. 123. 1539. 692. 195.
## 6 2011-06-01 00:00:00 3686. 151. 1637. 592. 101.
## 7 2011-07-01 00:00:00 4370. 107. 1791. 800. 143.
## 8 2011-08-01 00:00:00 3668. 184. 1256. 586. 134.
## 9 2011-09-01 00:00:00 12875. 606. 4172. 2176. 1014.
## 10 2011-10-01 00:00:00 4777. 158. 1941. 787. 341.
## # ... with 118 more rows, and 6 more variables: `Kep. Riau` <dbl>, Jambi <dbl>,
## # `Sumatera Selatan` <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # `Kep. Bangka Belitung` <dbl>dataoutflowperbulan <- read_excel(path = 'C:/Users/shafira halmahera/Documents/LINEAR ALGEBRA/DataPerbulanOutflow.xlsx')
dataoutflowperbulan## # A tibble: 128 x 12
## Bulan Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 3442. 350. 941. 307. 478.
## 2 2011-02-01 00:00:00 3989. 193. 990. 228. 400.
## 3 2011-03-01 00:00:00 4229. 230. 1209. 347. 621.
## 4 2011-04-01 00:00:00 6721. 529. 1653. 336. 1006.
## 5 2011-05-01 00:00:00 5787. 523. 1465. 328. 1000.
## 6 2011-06-01 00:00:00 7395. 406. 2167. 399. 1366.
## 7 2011-07-01 00:00:00 7154. 958. 1695. 449. 815.
## 8 2011-08-01 00:00:00 16043. 1046. 4104. 1376. 2729.
## 9 2011-09-01 00:00:00 1915. 124. 824. 148. 154.
## 10 2011-10-01 00:00:00 5174. 634. 1392. 299. 830.
## # ... with 118 more rows, and 6 more variables: `Kep. Riau` <dbl>, Jambi <dbl>,
## # `Sumatera Selatan` <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # `Kep. Bangka Belitung` <dbl>2.1 1. visualisasi dan prediksi data Inflow-Outflow di Kota Aceh
plot(datainflowtahun$Tahun, datainflowtahun$`Aceh`, type = "l", col = "red", main = 'Data Inflow-Outflow Uang Kartal di Aceh Setiap Tahun', xlab = 'Tahun Periode', ylab = 'Data Inflow-Outflow') lines(dataoutflowtahun$Tahun, dataoutflowtahun$`Aceh`, col = 'Green' ) legend('top', c("Inflow","Outlow"),fill=c("red","purple") )
plot(datainflowperbulan$Bulan, datainflowperbulan$`Aceh`, type = "l", col = "steelblue", main = 'Data Inflow-Outflow Uang Kartal di Aceh Setiap Bulan', xlab = 'Bulan', ylab = 'Data Inflow-Outflow') lines(dataoutflowperbulan$Bulan, dataoutflowperbulan$`Aceh`, col = 'purple') legend('right',c('Inflow','Outflow'),fill = c('steelblue','purple'))
Acehtimeseries <- datainflowperbulan$`Aceh` plot.ts(Acehtimeseries, type = "l", col = "red")
logAceh <- dataoutflowperbulan$`Aceh` plot.ts(logAceh, type = "l", col = "blue")
library(TTR)
## Warning: package 'TTR' was built under R version 4.1.2
AcehSMA3 <- SMA(datainflowperbulan$`Aceh`, n = 3)
plot.ts(AcehSMA3)
library('TTR')
AcehSMA3 <- SMA(datainflowperbulan$`Aceh`, n = 8)
plot.ts(AcehSMA3)
Acehinflowts <- ts(datainflowperbulan$`Aceh`, frequency = 12, start = c(2011,1)) Acehinflowts## Jan Feb Mar Apr May Jun ## 2011 122.18349 122.75253 151.37534 107.22432 183.84525 605.62334 ## 2012 139.33374 167.56600 119.32971 196.61835 420.06418 286.31394 ## 2013 3775.08977 3705.38033 3449.77565 3456.32173 8516.17096 243.91990 ## 2014 260.82180 168.17801 194.97802 173.99322 1306.11875 271.45458 ## 2015 263.06848 256.64615 398.59527 977.94399 495.56495 179.23767 ## 2016 218.98473 298.46423 450.32018 1374.47417 310.75050 538.99459 ## 2017 240.71874 299.60563 194.84441 1149.75614 264.01934 627.70230 ## 2018 262.95066 288.49282 1005.08498 784.64208 369.23511 426.04458 ## 2019 342.84300 420.44274 1554.92585 473.28934 684.81679 405.51614 ## 2020 281.42142 489.21304 1095.11262 257.81810 592.86464 410.39330 ## 2021 308.33410 566.54102 502.60975 280.32142 424.17610 ## Jul Aug Sep Oct Nov Dec ## 2011 157.64630 287.24653 176.19523 315.65341 292.57807 170.13069 ## 2012 142.89984 288.58842 80.49051 5571.15653 3457.27812 3253.57336 ## 2013 379.41362 322.99838 205.46842 779.00596 332.03457 248.89939 ## 2014 454.45573 219.11177 157.53593 836.57498 376.45107 317.53476 ## 2015 257.65850 227.20326 123.34945 883.11220 498.41373 242.45180 ## 2016 432.31664 301.61184 225.04199 1120.37553 452.83734 347.32016 ## 2017 365.36280 275.68807 175.99260 1279.35872 366.57150 278.86587 ## 2018 344.08223 243.18631 150.59965 1293.88334 565.87121 397.27368 ## 2019 467.20195 436.23339 466.53727 1641.95487 692.74998 297.06861 ## 2020 273.60601 438.09977 170.52803 762.78539 487.91516 368.91965 ## 2021Acehoutflowts <- ts(dataoutflowperbulan$`Aceh`, frequency = 12, start = c(2011,1)) Acehoutflowts## Jan Feb Mar Apr May Jun ## 2011 349.57673 192.62487 230.35748 528.59007 523.47023 405.84701 ## 2012 420.97459 217.70857 503.88607 600.25342 429.26734 606.25526 ## 2013 758.75245 1850.82994 2442.65886 1618.83372 2777.13063 2209.74012 ## 2014 288.10448 489.92887 504.83732 773.93194 485.84142 912.38357 ## 2015 269.29171 255.71308 521.69449 1125.85624 564.44034 1011.07770 ## 2016 307.32375 172.45727 730.75026 667.74179 1079.70825 2642.66616 ## 2017 247.27680 344.01370 677.88709 850.88747 1157.54011 2346.78323 ## 2018 120.03917 266.02669 996.17473 707.23188 1634.43230 1889.68997 ## 2019 85.36298 400.92165 964.22663 1218.54729 3312.30047 122.91218 ## 2020 182.38950 426.10026 1433.83382 1432.33902 1689.68700 436.00470 ## 2021 56.98918 60.56520 591.41875 1789.00566 2112.99646 176.09492 ## Jul Aug Sep Oct Nov Dec ## 2011 957.58488 1046.09243 123.98156 634.45751 595.14381 750.32697 ## 2012 600.85083 791.04331 303.81795 854.83456 207.40890 841.70726 ## 2013 5383.25551 2570.21842 566.69494 895.80335 699.91951 1504.22825 ## 2014 1538.16507 285.80192 611.06198 902.31059 586.60778 1250.89508 ## 2015 1558.53777 301.57675 1040.79937 316.39942 824.14892 1847.02405 ## 2016 692.69059 653.34617 1027.41562 515.70143 962.49370 1858.31057 ## 2017 282.09425 1520.49864 354.31840 667.42332 766.85433 2544.67010 ## 2018 278.98765 1155.80705 609.61619 549.33587 622.53291 2619.93560 ## 2019 687.35821 1230.78590 600.44006 552.87679 1055.67352 2855.56524 ## 2020 1768.67777 455.94178 829.58521 1174.85940 774.36491 2269.89617 ## 2021 662.04381 320.71844
plot.ts(Acehinflowts, col = 'blue')
lines(Acehoutflowts, col = 'red')
legend('right', c('Inflow','Outflow'),fill = c('grey','red'))
Acehintscomponents <- decompose(Acehinflowts)
Acehintscomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -95.49778 -62.42673 200.12086 240.50931 694.72915 -370.43198
## 2012 -95.49778 -62.42673 200.12086 240.50931 694.72915 -370.43198
## 2013 -95.49778 -62.42673 200.12086 240.50931 694.72915 -370.43198
## 2014 -95.49778 -62.42673 200.12086 240.50931 694.72915 -370.43198
## 2015 -95.49778 -62.42673 200.12086 240.50931 694.72915 -370.43198
## 2016 -95.49778 -62.42673 200.12086 240.50931 694.72915 -370.43198
## 2017 -95.49778 -62.42673 200.12086 240.50931 694.72915 -370.43198
## 2018 -95.49778 -62.42673 200.12086 240.50931 694.72915 -370.43198
## 2019 -95.49778 -62.42673 200.12086 240.50931 694.72915 -370.43198
## 2020 -95.49778 -62.42673 200.12086 240.50931 694.72915 -370.43198
## 2021 -95.49778 -62.42673 200.12086 240.50931 694.72915
## Jul Aug Sep Oct Nov Dec
## 2011 -369.35764 -395.44997 -509.58543 743.44204 45.60340 -121.65522
## 2012 -369.35764 -395.44997 -509.58543 743.44204 45.60340 -121.65522
## 2013 -369.35764 -395.44997 -509.58543 743.44204 45.60340 -121.65522
## 2014 -369.35764 -395.44997 -509.58543 743.44204 45.60340 -121.65522
## 2015 -369.35764 -395.44997 -509.58543 743.44204 45.60340 -121.65522
## 2016 -369.35764 -395.44997 -509.58543 743.44204 45.60340 -121.65522
## 2017 -369.35764 -395.44997 -509.58543 743.44204 45.60340 -121.65522
## 2018 -369.35764 -395.44997 -509.58543 743.44204 45.60340 -121.65522
## 2019 -369.35764 -395.44997 -509.58543 743.44204 45.60340 -121.65522
## 2020 -369.35764 -395.44997 -509.58543 743.44204 45.60340 -121.65522
## 2021Acehouttscomponents <- decompose(Acehoutflowts)
Acehouttscomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2012 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2013 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2014 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2015 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2016 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2017 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2018 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2019 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2020 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2021 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## Jul Aug Sep Oct Nov Dec
## 2011 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2012 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2013 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2014 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2015 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2016 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2017 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2018 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2019 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2020 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2021 414.184121 42.244469plot(Acehintscomponents$seasonal, type = 'l', col = 'turquoise') lines(Acehouttscomponents$seasonal, col = "orange") legend("right",c("Inflow","Outflow"),fill=c("turquoise","red"))
plot(Acehintscomponents$trend, type = 'l', col = 'red') lines(Acehouttscomponents$trend, col = 'green') legend('right', c("Inflow","Outflow"),fill=c("red","green"))
plot(Acehintscomponents$random, type = 'l', col = 'Skyblue') lines(Acehouttscomponents$random, col = 'purple') legend('right', c("Inflow","Outflow"),fill=c("skyblue","purple"))
plot(Acehintscomponents$figure, type = 'l', col = 'blue') lines(Acehouttscomponents$figure, col = 'green') legend('right', c("Inflow","Outflow"),fill=c("blue","green"))
Acehintsseasonallyadjusted <- Acehtimeseries - Acehintscomponents$seasonal plot(Acehintsseasonallyadjusted)
2.2 1. visualisasi dan prediksi data Inflow-Outflow di Sumatra
plot(datainflowtahun$Tahun, datainflowtahun$`Sumatera Utara`, type = "l", col = "blue", main = 'Data Inflow Uang Kartal di Sumatra Utara Setiap Tahun', xlab = 'Tahun Periode', ylab = 'Data Inflow-Outflow') lines(dataoutflowtahun$Tahun, dataoutflowtahun$`Sumatera Utara`, col = 'green') legend('top', c("Inflow","Outlow"),fill=c("blue","green"))
plot(datainflowperbulan$Bulan, datainflowperbulan$`Sumatera Utara`, type = "l", col = "steelblue", main = 'Data Inflow-Outflow Uang Kartal di Sumatera Utara Setiap Bulan', xlab = 'Bulan', ylab = 'Data Inflow-Outflow') lines(dataoutflowperbulan$Bulan, dataoutflowperbulan$`Sumatera Utara`, col = 'purple') legend('right',c('Inflow','Outflow'),fill = c('steelblue','yellow'))
Sumutts <- datainflowperbulan$`Sumatera Utara` plot.ts(Sumutts, type = "l", col = "yellow")
logsumut <- dataoutflowperbulan$`Sumatera Utara` plot.ts(logsumut, type = "l", col = "steelblue")
library(TTR) sumutSMA3 <- SMA(datainflowperbulan$`Sumatera Utara`, n = 3) plot.ts(sumutSMA3)
library('TTR') sumutSMA3 <- SMA(datainflowperbulan$`Sumatera Utara`, n = 8) plot.ts(sumutSMA3)
sumutinflowts <- ts(datainflowperbulan$`Sumatera Utara`, frequency = 12, start = c(2011,1))
sumutinflowts## Jan Feb Mar Apr May Jun Jul
## 2011 1429.1551 1539.2862 1636.5456 1791.1685 1255.7771 4171.7420 1940.8248
## 2012 1958.6650 2340.7850 1763.7063 2418.6288 3146.4553 2265.7190 1794.1134
## 2013 1005.6172 965.7285 915.6734 1064.5788 2923.1453 1883.2831 2061.2406
## 2014 2412.9589 2104.0928 2277.4757 1289.7373 6180.5783 2309.9967 2132.9841
## 2015 2118.8912 2064.3412 2195.4568 4316.3041 3070.8514 2205.2647 2172.2108
## 2016 2178.8535 2934.5122 1934.5506 6145.8753 2599.2919 2611.1014 2470.8154
## 2017 2445.6147 2868.9780 1696.6538 5829.4896 2961.9966 2729.2401 2687.0436
## 2018 3252.3518 2292.8486 5954.2310 4699.5902 3350.3244 3165.3554 3165.9388
## 2019 3839.8883 3018.5163 7840.8944 4193.3568 3573.5657 3295.2926 3680.2501
## 2020 2035.7135 2380.0784 4344.0487 3057.1278 2370.7092 2391.4783 1908.7555
## 2021 3669.9855 4948.0587 3752.2078 2368.1586 2461.1935
## Aug Sep Oct Nov Dec
## 2011 1942.8641 1608.2825 2979.0495 2137.5356 2034.3183
## 2012 1956.5636 1185.1042 2011.8480 1284.0139 986.4736
## 2013 1888.7141 1129.7521 3915.7218 2518.0923 1977.0690
## 2014 1911.6489 1472.3948 4313.0187 1833.3080 2167.4386
## 2015 2272.4471 1524.0474 4181.4312 2940.6137 2494.2178
## 2016 2171.7520 1763.8555 4297.6567 2983.0020 2741.7477
## 2017 2706.0655 1669.2277 5434.8880 2756.8291 2766.0818
## 2018 3130.5635 1799.6573 5704.4344 3720.4500 3145.5627
## 2019 3329.0633 1771.0466 6476.7265 3659.4621 2723.3467
## 2020 2826.9792 2434.8872 7420.0889 3749.5795 3470.9778
## 2021sumutoutflowts <- ts(dataoutflowperbulan$`Sumatera Utara`, frequency = 12, start = c(2011,1))
sumutoutflowts## Jan Feb Mar Apr May Jun Jul
## 2011 940.7270 990.2344 1208.7307 1652.7141 1464.7969 2167.0247 1695.1657
## 2012 984.0324 1216.1279 1787.1988 1807.6206 1874.7360 2688.0924 1964.9303
## 2013 385.7855 571.1485 981.3722 840.9738 1249.0038 1329.4347 3110.4414
## 2014 1386.2664 1401.1061 1758.2459 2054.0262 1829.6144 1703.7050 6389.1018
## 2015 572.9684 1763.7684 1389.7572 2303.6786 1510.8211 3233.5680 5255.8327
## 2016 1101.0264 1436.2692 1955.5644 2261.7604 2799.0006 7101.1630 1545.3956
## 2017 1381.2569 1585.6275 2219.8469 2436.9895 2995.6336 6666.8397 900.1986
## 2018 464.9380 2187.8200 2554.5897 2824.5938 4441.1976 6066.3074 2069.7335
## 2019 1254.4764 2323.8685 3046.6558 4576.5874 8856.8881 780.2976 3028.1933
## 2020 1456.3547 2150.2914 3244.4160 3371.2418 4147.5741 1473.5355 3525.8830
## 2021 767.5072 1758.2828 2249.6110 5490.0438 5183.1904 2210.7195 3486.2112
## Aug Sep Oct Nov Dec
## 2011 4103.7915 824.0580 1392.1819 1597.5122 4139.5386
## 2012 3120.9933 821.0337 1242.4665 1443.9081 3543.4771
## 2013 1837.2198 1362.0435 1608.7270 1880.8227 4077.7257
## 2014 793.8406 1397.2762 1888.7593 1700.0491 4088.9477
## 2015 982.1728 1852.0552 1907.6366 2126.0199 4978.8323
## 2016 1765.1649 2518.4152 2080.3687 2207.2962 5187.3478
## 2017 2908.6280 2161.4140 2247.3297 3283.9796 6455.4113
## 2018 2934.9619 1924.2260 2159.8890 2921.3840 6358.6489
## 2019 3577.9976 2629.2801 2576.9991 3782.3821 7617.2698
## 2020 3053.7066 2141.8995 3856.8591 2151.2443 9184.5577
## 2021 2307.3456plot.ts(sumutinflowts, col = 'blue') lines(sumutoutflowts, col = 'red') legend('right', c('Inflow','Outflow'),fill = c('blue','red'))
sumutintscomponents <- decompose(sumutinflowts) sumutintscomponents$seasonal## Jan Feb Mar Apr May Jun ## 2011 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2012 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2013 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2014 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2015 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2016 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2017 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2018 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2019 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2020 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2021 -407.66532 -442.57366 433.15186 863.43108 520.09971 ## Jul Aug Sep Oct Nov Dec ## 2011 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2012 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2013 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2014 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2015 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2016 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2017 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2018 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2019 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2020 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2021sumutouttscomponents <- decompose(sumutoutflowts) sumutouttscomponents$seasonal## Jan Feb Mar Apr May Jun ## 2011 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2012 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2013 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2014 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2015 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2016 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2017 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2018 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2019 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2020 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2021 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## Jul Aug Sep Oct Nov Dec ## 2011 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2012 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2013 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2014 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2015 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2016 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2017 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2018 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2019 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2020 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2021 389.85774 -53.26060plot(sumutintscomponents$seasonal, type = 'l', col = 'turquoise') lines(sumutouttscomponents$seasonal, col = "orange") legend("right",c("Inflow","Outflow"),fill=c("turquoise","orange"))
plot(sumutintscomponents$trend, type = 'l', col = 'red') lines(sumutouttscomponents$trend, col = 'green') legend('right', c("Inflow","Outflow"),fill=c("red","black"))
plot(sumutintscomponents$random, type = 'l', col = 'Skyblue') lines(sumutouttscomponents$random, col = 'purple') legend('right', c("Inflow","Outflow"),fill=c("skyblue","purple"))
plot(sumutintscomponents$figure, type = 'l', col = 'blue') lines(sumutouttscomponents$figure, col = 'green') legend('right', c("Inflow","Outflow"),fill=c("blue","green"))
sumutintsseasonallyadjusted <- Sumutts - sumutintscomponents$seasonal plot(sumutintsseasonallyadjusted)
1. visualisasi dan prediksi data Inflow-Outflow di Sumatra Barat
plot(datainflowtahun$Tahun, datainflowtahun$`Sumatera Barat`,type = "l", col = "orange", main = 'Data Inflow Uang Kartal di Sumatra Barat Setiap Tahun', xlab = 'Tahun Periode', ylab = 'Data Inflow') lines(dataoutflowtahun$Tahun, dataoutflowtahun$`Sumatera Barat`, col = 'purple') legend('top', c("Inflow","Outlow"),fill=c("orange","purple"))
plot(datainflowperbulan$Bulan, datainflowperbulan$`Sumatera Barat`, type = "l", col = "steelblue", main = 'Data Inflow-Outflow Uang Kartal di Sumatera Utara Setiap Bulan', xlab = 'Bulan', ylab = 'Data Inflow-Outflow') lines(dataoutflowperbulan$Bulan, dataoutflowperbulan$`Sumatera Barat`, col = 'purple') legend('right',c('Inflow','Outflow'),fill = c('steelblue','purple'))
Sumbarts <- datainflowperbulan$`Sumatera Barat` plot.ts(Sumbarts, type = "l", col = "yellow")
logsumbar <- dataoutflowperbulan$`Sumatera Barat` plot.ts(logsumbar, type = "l", col = "steelblue")
library(TTR) sumbarSMA3 <- SMA(datainflowperbulan$`Sumatera Barat`, n = 3) plot.ts(sumbarSMA3)
library('TTR') sumbarSMA3 <- SMA(datainflowperbulan$`Sumatera Barat`, n = 8) plot.ts(sumbarSMA3)
sumbarinflowts <- ts(datainflowperbulan$`Sumatera Barat`, frequency = 12, start = c(2011,1)) sumbarinflowts## Jan Feb Mar Apr May Jun Jul ## 2011 539.1026 691.9377 592.4192 799.5802 586.3581 2176.2413 787.3761 ## 2012 704.9590 885.0385 641.2570 1038.4298 1339.7732 1507.8169 789.7558 ## 2013 994.6862 1107.1890 1086.4650 1303.0975 2173.6578 1202.3046 933.7316 ## 2014 993.2328 762.4694 866.8874 675.1555 3114.2115 1200.3284 1157.9625 ## 2015 836.3249 855.4427 1045.4934 2161.9387 1729.1363 824.0283 995.3346 ## 2016 737.9714 919.1321 720.4721 2928.9035 1145.6062 1048.3006 1050.2491 ## 2017 1037.7823 1173.4844 683.3602 2902.9224 1503.0438 1122.1439 1047.2614 ## 2018 1043.6172 956.1836 2214.6015 2449.9422 1185.0947 1199.5619 1008.1251 ## 2019 986.2518 810.4107 3290.2635 1379.9442 1194.5156 1066.1918 1093.7082 ## 2020 586.1949 460.8289 1752.8809 720.9419 934.1740 842.2214 604.4694 ## 2021 924.2523 2033.1787 1301.2214 934.1477 1017.1201 ## Aug Sep Oct Nov Dec ## 2011 854.4358 513.2068 1130.4905 865.3519 854.9514 ## 2012 883.7977 550.4838 1776.9203 1112.8960 940.8829 ## 2013 875.4979 548.6130 1675.2029 1111.3808 924.0093 ## 2014 931.1027 691.0219 1698.0899 904.5427 969.6610 ## 2015 750.3287 538.4899 1751.8196 892.1499 904.6083 ## 2016 1005.0248 973.9955 1850.5169 1143.2622 1287.3335 ## 2017 883.3420 677.3816 2037.4366 957.8346 732.3303 ## 2018 776.0709 497.4198 1890.0168 845.6557 917.9565 ## 2019 771.6151 503.1632 1936.5593 867.9322 593.6931 ## 2020 893.2831 502.3578 2463.1456 1078.7217 996.1128 ## 2021
sumbaroutflowts <- ts(dataoutflowperbulan$`Sumatera Barat`, frequency = 12, start = c(2011,1)) sumbaroutflowts## Jan Feb Mar Apr May Jun ## 2011 306.70068 227.74199 347.23365 335.95990 327.77383 399.24039 ## 2012 214.52616 252.76902 462.17950 577.54488 461.72280 623.94257 ## 2013 245.10797 218.45108 398.34203 317.45463 461.02830 471.02622 ## 2014 185.88126 273.86294 480.13567 452.26115 466.95347 548.54011 ## 2015 124.28159 443.52843 443.34413 514.88579 503.17081 926.50648 ## 2016 140.03323 351.99398 316.41743 604.36993 757.45169 2598.20471 ## 2017 349.10531 710.49354 848.72339 860.68821 999.67421 3176.59985 ## 2018 55.96053 302.53616 543.51806 570.24349 1461.73993 2601.75460 ## 2019 75.55494 370.26231 613.28838 952.67623 3692.93346 50.39067 ## 2020 102.48174 308.36325 782.28278 819.13541 2242.07887 34.07573 ## 2021 86.54225 374.74081 559.24066 1554.62334 2167.68623 295.68386 ## Jul Aug Sep Oct Nov Dec ## 2011 448.56438 1376.25990 147.70279 298.57216 349.75474 734.22520 ## 2012 543.65577 1260.36359 163.22296 437.83317 405.63471 1030.89819 ## 2013 1130.65362 773.18744 411.62158 536.88884 421.89894 1125.35118 ## 2014 2100.82357 115.32964 393.25698 416.17580 555.13227 1071.69548 ## 2015 2153.22221 161.12169 337.86600 346.21304 452.70749 1063.81167 ## 2016 636.60428 298.35824 592.36023 470.20911 815.03093 1616.78339 ## 2017 151.96773 583.16929 372.26254 511.67734 738.88167 1451.21128 ## 2018 113.42245 401.53968 287.98036 398.91845 512.61803 1196.57690 ## 2019 445.31828 672.32642 403.02094 428.11685 511.72653 1249.35115 ## 2020 651.14472 565.58335 343.19704 792.57966 483.75028 1638.08473 ## 2021 684.83394 217.18849
plot.ts(sumbarinflowts, col = 'blue') lines(sumbaroutflowts, col = 'red') legend('right', c('Inflow','Outflow'),fill = c('blue','red'))
sumbarintscomponents <- decompose(sumutinflowts) sumbarintscomponents$seasonal## Jan Feb Mar Apr May Jun ## 2011 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2012 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2013 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2014 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2015 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2016 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2017 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2018 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2019 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2020 -407.66532 -442.57366 433.15186 863.43108 520.09971 -307.36877 ## 2021 -407.66532 -442.57366 433.15186 863.43108 520.09971 ## Jul Aug Sep Oct Nov Dec ## 2011 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2012 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2013 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2014 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2015 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2016 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2017 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2018 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2019 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2020 -380.38906 -391.68067 -1192.53974 1833.90171 -88.72274 -439.64441 ## 2021sumbarouttscomponents <- decompose(sumutoutflowts) sumbarouttscomponents$seasonal## Jan Feb Mar Apr May Jun ## 2011 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2012 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2013 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2014 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2015 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2016 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2017 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2018 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2019 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2020 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## 2021 -1668.31245 -1004.32016 -452.48739 -76.68515 712.34043 835.13962 ## Jul Aug Sep Oct Nov Dec ## 2011 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2012 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2013 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2014 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2015 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2016 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2017 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2018 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2019 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2020 389.85774 -53.26060 -805.47535 -492.84969 -310.99376 2927.04677 ## 2021 389.85774 -53.26060
plot(sumbarintscomponents$seasonal, type = 'l', col = 'turquoise') lines(sumbarouttscomponents$seasonal, col = "orange") legend("right",c("Inflow","Outflow"),fill=c("turquoise","orange"))
plot(sumbarintscomponents$seasonal, type = 'l', col = 'turquoise') lines(sumbarouttscomponents$seasonal, col = "orange") legend("right",c("Inflow","Outflow"),fill=c("turquoise","orange"))
plot(sumbarintscomponents$random, type = 'l', col = 'Skyblue') lines(sumbarouttscomponents$random, col = 'purple') legend('right', c("Inflow","Outflow"),fill=c("skyblue","purple"))
plot(sumbarintscomponents$figure, type = 'l', col = 'blue') lines(sumbarouttscomponents$figure, col = 'green') legend('right', c("Inflow","Outflow"),fill=c("blue","green"))
sumbarintsseasonallyadjusted <- Sumbarts - sumbarintscomponents$seasonal plot(sumbarintsseasonallyadjusted)
2.3 1. visualisasi dan prediksi data Inflow-Outflow di Wilayah Sumatra
plot(dataoutflowtahun$Tahun, dataoutflowtahun$`Sumatera`, type = "l", col = "blue", main = 'Data Inflow dan Outflow Uang Kartal di Sumatera Setiap Tahun', xlab = 'Tahun Periode', ylab = 'Data Inflow dan Outflow')
lines(datainflowtahun$Tahun, datainflowtahun$`Sumatera`, col = 'red')
legend('top', c("Outlow","Inflow"),fill=c("blue","red"))
plot(datainflowperbulan$Bulan, datainflowperbulan$`Sumatera`, type = "l", col = "steelblue", main = 'Data Inflow-Outflow Uang Kartal di Sumatera Setiap Bulan', xlab = 'Bulan', ylab = 'Data Inflow-Outflow')
lines(dataoutflowperbulan$Bulan, dataoutflowperbulan$`Sumatera`, col = 'purple')
legend('right',c('Inflow','Outflow'),fill = c('steelblue','purple'))
Sumaterats <- datainflowperbulan$`Sumatera`
plot.ts(Sumaterats, type = "l", col = "yellow")
logsumatera <- dataoutflowperbulan$`Sumatera`
plot.ts(logsumatera, type = "l", col = "orange")
library(TTR) sumateraSMA3 <- SMA(datainflowperbulan$`Sumatera`, n = 3) plot.ts(sumateraSMA3)
library('TTR') sumateraSMA3 <- SMA(datainflowperbulan$`Sumatera`, n = 8) plot.ts(sumateraSMA3)
sumaterainflowts <- ts(datainflowperbulan$`Sumatera`, frequency = 12, start = c(2011,1)) sumaterainflowts## Jan Feb Mar Apr May Jun Jul ## 2011 3156.548 3821.275 3686.394 4369.643 3668.498 12874.594 4776.883 ## 2012 4102.575 5321.981 4064.952 5489.699 9422.659 6813.338 4563.922 ## 2013 8267.947 7623.367 6961.815 7552.290 19523.108 5265.619 6181.279 ## 2014 5988.730 4921.418 5591.202 3440.204 19746.407 6305.927 6798.485 ## 2015 4954.255 5358.552 5936.657 15050.224 8915.131 5710.106 6763.497 ## 2016 5254.795 6761.434 5066.314 20548.504 6548.412 7498.570 6952.295 ## 2017 6638.224 7317.874 4071.240 21208.720 8722.990 8250.898 7610.729 ## 2018 7628.992 5973.344 19402.076 14326.890 9119.047 8886.660 8429.308 ## 2019 9406.456 7523.072 26667.739 11014.410 10707.883 9462.332 10195.256 ## 2020 6536.998 7788.132 14946.781 8278.451 8012.437 7559.106 5735.149 ## 2021 9095.626 16275.454 10211.629 6787.420 7085.136 ## Aug Sep Oct Nov Dec ## 2011 5669.993 3496.335 7371.435 5443.242 5022.248 ## 2012 5452.494 2842.029 13436.780 8035.017 7017.142 ## 2013 5347.888 3157.046 11612.677 6964.701 5238.644 ## 2014 5515.775 3899.380 12838.000 5173.687 5600.472 ## 2015 6087.162 4161.556 13692.690 7760.507 6313.597 ## 2016 6098.330 5268.100 12734.711 7752.686 7568.883 ## 2017 7122.755 4748.106 16240.858 7668.179 7130.231 ## 2018 8078.990 4610.368 17413.907 9281.546 8215.984 ## 2019 8492.726 5380.872 19330.620 10365.349 7128.873 ## 2020 8462.623 5200.062 21182.291 9983.745 8648.731 ## 2021
sumateraoutflowts <- ts(dataoutflowperbulan$`Sumatera`, frequency = 12, start = c(2011,1)) sumateraoutflowts## Jan Feb Mar Apr May Jun Jul ## 2011 3441.614 3989.113 4228.628 6721.276 5787.181 7394.536 7154.223 ## 2012 3200.178 4100.054 6605.179 6665.551 7147.179 8560.319 7711.993 ## 2013 2221.436 4621.158 8219.574 4613.748 8423.251 7790.216 17485.108 ## 2014 4289.908 4820.657 7088.166 8015.452 7757.313 8157.185 24722.650 ## 2015 2036.392 5682.352 6300.508 10051.597 7592.788 12421.852 22934.645 ## 2016 2804.053 4909.740 6985.628 8649.278 10859.812 28813.953 6455.632 ## 2017 4855.706 6495.905 9234.822 9234.883 11638.176 29889.710 3252.637 ## 2018 2424.451 7487.879 10455.312 9952.146 19165.027 25439.136 6324.910 ## 2019 3735.569 7719.811 11089.472 15127.060 37664.505 2465.417 10575.813 ## 2020 4693.754 6958.705 12667.832 11775.906 19644.928 3971.849 12710.177 ## 2021 1990.678 6099.024 9638.351 19930.265 22004.413 7748.386 11650.666 ## Aug Sep Oct Nov Dec ## 2011 16042.967 1914.778 5173.616 5609.913 12634.335 ## 2012 13610.489 3180.756 6273.015 5018.851 13161.070 ## 2013 10207.967 6806.163 8014.259 8355.285 16529.555 ## 2014 2377.454 6171.922 7655.389 7005.319 14276.798 ## 2015 4668.410 6733.060 5783.016 8056.207 16924.980 ## 2016 7937.744 10071.108 7571.519 9563.416 17369.845 ## 2017 11015.435 6693.301 8559.331 12083.466 20652.339 ## 2018 10042.081 7060.453 8155.825 9944.104 19225.025 ## 2019 11780.050 8535.685 8868.257 12462.606 23460.023 ## 2020 9744.450 9247.241 14431.727 9435.013 25307.224 ## 2021 7565.509
plot.ts(sumaterainflowts, col = 'blue')
lines(sumateraoutflowts, col = 'red')
legend('right', c('Inflow','Outflow'),fill = c('black','red'))
sumateraintscomponents <- decompose(sumaterainflowts) sumateraintscomponents$seasonal## Jan Feb Mar Apr May Jun ## 2011 -1755.1280 -1793.5819 1976.6683 3482.6880 2709.8038 -1213.0639 ## 2012 -1755.1280 -1793.5819 1976.6683 3482.6880 2709.8038 -1213.0639 ## 2013 -1755.1280 -1793.5819 1976.6683 3482.6880 2709.8038 -1213.0639 ## 2014 -1755.1280 -1793.5819 1976.6683 3482.6880 2709.8038 -1213.0639 ## 2015 -1755.1280 -1793.5819 1976.6683 3482.6880 2709.8038 -1213.0639 ## 2016 -1755.1280 -1793.5819 1976.6683 3482.6880 2709.8038 -1213.0639 ## 2017 -1755.1280 -1793.5819 1976.6683 3482.6880 2709.8038 -1213.0639 ## 2018 -1755.1280 -1793.5819 1976.6683 3482.6880 2709.8038 -1213.0639 ## 2019 -1755.1280 -1793.5819 1976.6683 3482.6880 2709.8038 -1213.0639 ## 2020 -1755.1280 -1793.5819 1976.6683 3482.6880 2709.8038 -1213.0639 ## 2021 -1755.1280 -1793.5819 1976.6683 3482.6880 2709.8038 ## Jul Aug Sep Oct Nov Dec ## 2011 -1435.0086 -1679.4540 -4115.0231 6156.7258 -610.1152 -1724.5112 ## 2012 -1435.0086 -1679.4540 -4115.0231 6156.7258 -610.1152 -1724.5112 ## 2013 -1435.0086 -1679.4540 -4115.0231 6156.7258 -610.1152 -1724.5112 ## 2014 -1435.0086 -1679.4540 -4115.0231 6156.7258 -610.1152 -1724.5112 ## 2015 -1435.0086 -1679.4540 -4115.0231 6156.7258 -610.1152 -1724.5112 ## 2016 -1435.0086 -1679.4540 -4115.0231 6156.7258 -610.1152 -1724.5112 ## 2017 -1435.0086 -1679.4540 -4115.0231 6156.7258 -610.1152 -1724.5112 ## 2018 -1435.0086 -1679.4540 -4115.0231 6156.7258 -610.1152 -1724.5112 ## 2019 -1435.0086 -1679.4540 -4115.0231 6156.7258 -610.1152 -1724.5112 ## 2020 -1435.0086 -1679.4540 -4115.0231 6156.7258 -610.1152 -1724.5112 ## 2021sumateraouttscomponents <- decompose(sumateraoutflowts) sumateraouttscomponents$seasonal## Jan Feb Mar Apr May ## 2011 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681 ## 2012 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681 ## 2013 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681 ## 2014 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681 ## 2015 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681 ## 2016 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681 ## 2017 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681 ## 2018 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681 ## 2019 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681 ## 2020 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681 ## 2021 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681 ## Jun Jul Aug Sep Oct ## 2011 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549 ## 2012 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549 ## 2013 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549 ## 2014 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549 ## 2015 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549 ## 2016 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549 ## 2017 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549 ## 2018 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549 ## 2019 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549 ## 2020 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549 ## 2021 4146.113221 2196.088359 3.268476 ## Nov Dec ## 2011 -1217.537318 7914.117948 ## 2012 -1217.537318 7914.117948 ## 2013 -1217.537318 7914.117948 ## 2014 -1217.537318 7914.117948 ## 2015 -1217.537318 7914.117948 ## 2016 -1217.537318 7914.117948 ## 2017 -1217.537318 7914.117948 ## 2018 -1217.537318 7914.117948 ## 2019 -1217.537318 7914.117948 ## 2020 -1217.537318 7914.117948 ## 2021plot(sumateraintscomponents$seasonal, type = 'l', col = 'turquoise') lines(sumateraouttscomponents$seasonal, col = "orange") legend("right",c("Inflow","Outflow"),fill=c("turquoise","orange"))
plot(sumateraintscomponents$trend, type = 'l', col = 'red') lines(sumateraouttscomponents$trend, col = 'green') legend('right', c("Inflow","Outflow"),fill=c("red","green"))
plot(sumateraintscomponents$random, type = 'l', col = 'Skyblue') lines(sumateraouttscomponents$random, col = 'purple') legend('right', c("Inflow","Outflow"),fill=c("skyblue","purple"))
plot(sumateraintscomponents$figure, type = 'l', col = 'blue') lines(sumateraouttscomponents$figure, col = 'green') legend('right', c("Inflow","Outflow"),fill=c("blue","green"))
sumateraintsseasonallyadjusted <- Sumaterats - sumateraintscomponents$seasonal plot(sumateraintsseasonallyadjusted)
sumaterasforecast <- HoltWinters(Sumaterats, beta=FALSE, gamma=FALSE)
sumaterasforecast## Holt-Winters exponential smoothing without trend and without seasonal component.
##
## Call:
## HoltWinters(x = Sumaterats, beta = FALSE, gamma = FALSE)
##
## Smoothing parameters:
## alpha: 0.07627218
## beta : FALSE
## gamma: FALSE
##
## Coefficients:
## [,1]
## a 9833.919sumaterasforecast$fitted## Time Series:
## Start = 2
## End = 125
## Frequency = 1
## xhat level
## 2 3156.548 3156.548
## 3 3207.249 3207.249
## 4 3243.794 3243.794
## 5 3329.665 3329.665
## 6 3355.508 3355.508
## 7 4081.550 4081.550
## 8 4134.584 4134.584
## 9 4251.693 4251.693
## 10 4194.081 4194.081
## 11 4436.424 4436.424
## 12 4513.216 4513.216
## 13 4552.041 4552.041
## 14 4517.760 4517.760
## 15 4579.099 4579.099
## 16 4539.884 4539.884
## 17 4612.329 4612.329
## 18 4979.223 4979.223
## 19 5119.115 5119.115
## 20 5076.769 5076.769
## 21 5105.427 5105.427
## 22 4932.792 4932.792
## 23 5581.410 5581.410
## 24 5768.552 5768.552
## 25 5863.785 5863.785
## 26 6047.155 6047.155
## 27 6167.376 6167.376
## 28 6227.970 6227.970
## 29 6328.979 6328.979
## 30 7335.324 7335.324
## 31 7177.463 7177.463
## 32 7101.482 7101.482
## 33 6967.731 6967.731
## 34 6677.082 6677.082
## 35 7053.531 7053.531
## 36 7046.755 7046.755
## 37 6908.847 6908.847
## 38 6838.667 6838.667
## 39 6692.435 6692.435
## 40 6608.441 6608.441
## 41 6366.793 6366.793
## 42 7387.285 7387.285
## 43 7304.808 7304.808
## 44 7266.189 7266.189
## 45 7132.681 7132.681
## 46 6886.070 6886.070
## 47 7340.037 7340.037
## 48 7174.805 7174.805
## 49 7054.727 7054.727
## 50 6894.519 6894.519
## 51 6777.368 6777.368
## 52 6713.245 6713.245
## 53 7349.124 7349.124
## 54 7468.567 7468.567
## 55 7334.446 7334.446
## 56 7290.898 7290.898
## 57 7199.087 7199.087
## 58 6967.407 6967.407
## 59 7480.359 7480.359
## 60 7501.727 7501.727
## 61 7411.106 7411.106
## 62 7246.639 7246.639
## 63 7209.631 7209.631
## 64 7046.156 7046.156
## 65 8076.009 8076.009
## 66 7959.496 7959.496
## 67 7924.340 7924.340
## 68 7850.200 7850.200
## 69 7716.581 7716.581
## 70 7529.830 7529.830
## 71 7926.818 7926.818
## 72 7913.537 7913.537
## 73 7887.249 7887.249
## 74 7791.983 7791.983
## 75 7755.822 7755.822
## 76 7474.791 7474.791
## 77 8522.307 8522.307
## 78 8537.614 8537.614
## 79 8515.746 8515.746
## 80 8446.718 8446.718
## 81 8345.736 8345.736
## 82 8071.337 8071.337
## 83 8694.444 8694.444
## 84 8616.169 8616.169
## 85 8502.833 8502.833
## 86 8436.183 8436.183
## 87 8248.337 8248.337
## 88 9099.057 9099.057
## 89 9497.795 9497.795
## 90 9468.907 9468.907
## 91 9424.498 9424.498
## 92 9348.593 9348.593
## 93 9251.757 9251.757
## 94 8897.749 8897.749
## 95 9547.295 9547.295
## 96 9527.025 9527.025
## 97 9427.029 9427.029
## 98 9425.460 9425.460
## 99 9280.361 9280.361
## 100 10606.534 10606.534
## 101 10637.644 10637.644
## 102 10643.001 10643.001
## 103 10552.949 10552.949
## 104 10525.667 10525.667
## 105 10370.610 10370.610
## 106 9990.032 9990.032
## 107 10702.459 10702.459
## 108 10676.747 10676.747
## 109 10406.143 10406.143
## 110 10111.035 10111.035
## 111 9933.862 9933.862
## 112 10316.208 10316.208
## 113 10160.784 10160.784
## 114 9996.925 9996.925
## 115 9810.987 9810.987
## 116 9500.114 9500.114
## 117 9420.982 9420.982
## 118 9099.043 9099.043
## 119 10020.659 10020.659
## 120 10017.844 10017.844
## 121 9913.418 9913.418
## 122 9851.044 9851.044
## 123 10341.047 10341.047
## 124 10331.176 10331.176
## 125 10060.886 10060.886plot(sumaterasforecast)
sumaterasforecast$SSE## [1] 2371546657sumaterasforecast <- HoltWinters(sumateraintscomponents$seasonal, beta = FALSE, gamma = FALSE)
sumaterasforecast## Holt-Winters exponential smoothing without trend and without seasonal component.
##
## Call:
## HoltWinters(x = sumateraintscomponents$seasonal, beta = FALSE, gamma = FALSE)
##
## Smoothing parameters:
## alpha: 0.04038928
## beta : FALSE
## gamma: FALSE
##
## Coefficients:
## [,1]
## a 165.2801sumaterasforecast$fitted## xhat level
## Feb 2011 -1755.127976 -1755.127976
## Mar 2011 -1756.681102 -1756.681102
## Apr 2011 -1605.893818 -1605.893818
## May 2011 -1400.369680 -1400.369680
## Jun 2011 -1234.362745 -1234.362745
## Jul 2011 -1233.502501 -1233.502501
## Aug 2011 -1241.641188 -1241.641188
## Sep 2011 -1259.324130 -1259.324130
## Oct 2011 -1374.663747 -1374.663747
## Nov 2011 -1070.476371 -1070.476371
## Dec 2011 -1051.882719 -1051.882719
## Jan 2012 -1079.049697 -1079.049697
## Feb 2012 -1106.356009 -1106.356009
## Mar 2012 -1134.112565 -1134.112565
## Apr 2012 -1008.470375 -1008.470375
## May 2012 -827.075737 -827.075737
## Jun 2012 -684.223729 -684.223729
## Jul 2012 -705.583202 -705.583202
## Aug 2012 -735.044168 -735.044168
## Sep 2012 -773.188197 -773.188197
## Oct 2012 -908.162493 -908.162493
## Nov 2012 -622.816765 -622.816765
## Dec 2012 -622.303760 -622.303760
## Jan 2013 -666.821122 -666.821122
## Feb 2013 -710.777048 -710.777048
## Mar 2013 -754.510752 -754.510752
## Apr 2013 -644.200404 -644.200404
## May 2013 -477.518367 -477.518367
## Jun 2013 -348.784729 -348.784729
## Jul 2013 -383.692340 -383.692340
## Aug 2013 -426.154245 -426.154245
## Sep 2013 -476.774115 -476.774115
## Oct 2013 -623.720361 -623.720361
## Nov 2013 -349.863045 -349.863045
## Dec 2013 -360.374443 -360.374443
## Jan 2014 -415.470940 -415.470940
## Feb 2014 -469.578719 -469.578719
## Mar 2014 -523.054249 -523.054249
## Apr 2014 -422.092262 -422.092262
## May 2014 -264.381011 -264.381011
## Jun 2014 -144.255837 -144.255837
## Jul 2014 -187.424222 -187.424222
## Aug 2014 -237.813254 -237.813254
## Sep 2014 -296.040081 -296.040081
## Oct 2014 -450.286044 -450.286044
## Nov 2014 -183.433614 -183.433614
## Dec 2014 -200.666977 -200.666977
## Jan 2015 -262.213943 -262.213943
## Feb 2015 -322.511661 -322.511661
## Mar 2015 -381.927123 -381.927123
## Apr 2015 -286.665158 -286.665158
## May 2015 -134.423711 -134.423711
## Jun 2015 -19.547418 -19.547418
## Jul 2015 -67.752686 -67.752686
## Aug 2015 -122.975164 -122.975164
## Sep 2015 -185.840218 -185.840218
## Oct 2015 -344.537074 -344.537074
## Nov 2015 -81.955769 -81.955769
## Dec 2015 -103.287748 -103.287748
## Jan 2016 -168.767791 -168.767791
## Feb 2016 -232.839731 -232.839731
## Mar 2016 -295.876978 -295.876978
## Apr 2016 -204.090516 -204.090516
## May 2016 -55.184199 -55.184199
## Jun 2016 56.491668 56.491668
## Jul 2016 5.215236 5.215236
## Aug 2016 -52.954364 -52.954364
## Sep 2016 -118.647508 -118.647508
## Oct 2016 -280.058228 -280.058228
## Nov 2016 -20.081177 -20.081177
## Dec 2016 -43.912227 -43.912227
## Jan 2017 -111.790404 -111.790404
## Feb 2017 -178.163620 -178.163620
## Mar 2017 -243.409194 -243.409194
## Apr 2017 -153.741869 -153.741869
## May 2017 -6.869097 -6.869097
## Jun 2017 102.855358 102.855358
## Jul 2017 49.706330 49.706330
## Aug 2017 -10.260233 -10.260233
## Sep 2017 -77.677762 -77.677762
## Oct 2017 -240.743221 -240.743221
## Nov 2017 17.645925 17.645925
## Dec 2017 -7.708894 -7.708894
## Jan 2018 -77.049298 -77.049298
## Feb 2018 -144.825682 -144.825682
## Mar 2018 -211.417752 -211.417752
## Apr 2018 -123.042537 -123.042537
## May 2018 22.590311 22.590311
## Jun 2018 131.124921 131.124921
## Jul 2018 76.834106 76.834106
## Aug 2018 15.771872 15.771872
## Sep 2018 -52.697074 -52.697074
## Oct 2018 -216.771486 -216.771486
## Nov 2018 40.649460 40.649460
## Dec 2018 14.365544 14.365544
## Jan 2019 -55.866430 -55.866430
## Feb 2019 -124.498375 -124.498375
## Mar 2019 -191.911450 -191.911450
## Apr 2019 -104.324081 -104.324081
## May 2019 40.552742 40.552742
## Jun 2019 148.361863 148.361863
## Jul 2019 93.374861 93.374861
## Aug 2019 31.644557 31.644557
## Sep 2019 -37.465476 -37.465476
## Oct 2019 -202.155080 -202.155080
## Nov 2019 54.675519 54.675519
## Dec 2019 27.825101 27.825101
## Jan 2020 -42.950495 -42.950495
## Feb 2020 -112.104105 -112.104105
## Mar 2020 -180.017776 -180.017776
## Apr 2020 -92.910783 -92.910783
## May 2020 51.505065 51.505065
## Jun 2020 158.871829 158.871829
## Jul 2020 103.460337 103.460337
## Aug 2020 41.322688 41.322688
## Sep 2020 -28.178237 -28.178237
## Oct 2020 -193.242946 -193.242946
## Nov 2020 63.227698 63.227698
## Dec 2020 36.031864 36.031864
## Jan 2021 -35.075197 -35.075197
## Feb 2021 -104.546885 -104.546885
## Mar 2021 -172.765786 -172.765786
## Apr 2021 -85.951697 -85.951697
## May 2021 58.183079 58.183079plot(sumaterasforecast)
sumaterasforecast$SSE## [1] 1050129697
library(forecast)dftimeseries.hw <- HoltWinters(Sumaterats, gamma = FALSE) sumateratsforecasts2 <- forecast(dftimeseries.hw, h = 20) sumateratsforecasts2## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 ## 126 8610.430 2865.0876 14355.77 -176.3132 17397.17 ## 127 8521.318 2761.9865 14280.65 -286.8193 17329.45 ## 128 8432.205 2650.6877 14213.72 -409.8629 17274.27 ## 129 8343.093 2529.3936 14156.79 -548.1928 17234.38 ## 130 8253.980 2396.3922 14111.57 -704.4276 17212.39 ## 131 8164.868 2250.0852 14079.65 -881.0115 17210.75 ## 132 8075.755 2089.0174 14062.49 -1080.1700 17231.68 ## 133 7986.643 1911.9038 14061.38 -1303.8687 17277.15 ## 134 7897.530 1717.6506 14077.41 -1553.7799 17348.84 ## 135 7808.417 1505.3716 14111.46 -1831.2593 17448.09 ## 136 7719.305 1274.3944 14164.22 -2137.3351 17575.94 ## 137 7630.192 1024.2596 14236.13 -2472.7100 17733.09 ## 138 7541.080 754.7108 14327.45 -2837.7760 17919.94 ## 139 7451.967 465.6788 14438.26 -3232.6389 18136.57 ## 140 7362.855 157.2596 14568.45 -3657.1519 18382.86 ## 141 7273.742 -170.3100 14717.79 -4110.9531 18658.44 ## 142 7184.630 -516.6786 14885.94 -4593.5049 18962.76 ## 143 7095.517 -881.4056 15072.44 -5104.1334 19295.17 ## 144 7006.404 -1263.9848 15276.79 -5642.0645 19654.87 ## 145 6917.292 -1663.8658 15498.45 -6206.4562 20041.04
plot(sumateratsforecasts2)
sumateratsdiff1 <- diff(Sumaterats, differences = 1) plot.ts(sumateratsdiff1)
acf(sumateratsdiff1, lag.max = 20)
