Komparasi Visualisasi Prediksi Data Inflow-Outflow Uang Kartal antara Sumatera Selatan dengan Riau
Najah Muchsin Sanin
8 Februari 2022
Dosen Pengampu : Prof. Dr. Suhartono, M.Kom
Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Jurusan : Teknik Informatika
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 antara Sumatera Selatan dengan Riau menggunakan bahasa pemrograman R.
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2datainflow <- read_excel(path = "inflowdata.xlsx")
datainflow## # A tibble: 11 x 12
## Tahun Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau `Kep. Riau`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 57900. 2308. 23238. 9385. 3012. 1426.
## 2 2012 65911. 2620. 25981. 11192. 4447. 2236.
## 3 2013 98369. 36337. 18120. 14056. 8933. 3378.
## 4 2014 86024. 4567. 30503. 14103. 6358. 2563.
## 5 2015 86549. 4710. 30254. 13309. 7156. 3218.
## 6 2016 97764. 5775. 34427. 14078. 8211. 4317.
## 7 2017 103748. 5514. 35617. 15312. 8553. 4412.
## 8 2018 117495. 5799. 41769. 15058. 10730. 5134.
## 9 2019 133762. 7509. 47112. 14750. 10915. 6077.
## 10 2020 109345. 6641. 36609. 10696. 9148. 6175.
## 11 2021 89270. 3702. 31840. 10748. 7769. 5009.
## # ... with 5 more variables: Jambi <dbl>, Sumatera Selatan <dbl>,
## # Bengkulu <dbl>, Lampung <dbl>, Kep. Bangka Belitung <dbl>library(readxl)
dataoutflow <- read_excel(path = "outflowdata.xlsx")
dataoutflow## # A tibble: 11 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.
## # ... with 5 more variables: Jambi <dbl>, Sumatera Selatan <dbl>,
## # Bengkulu <dbl>, Lampung <dbl>, Kep. Bangka Belitung <dbl>1. Komparasi Visualisasi Prediksi Data Inflow Uang Kartal antara Sumatera Selatan dengan Riau Setiap Periode
plot(datainflow$Tahun,datainflow$`Sumatera Selatan`,type = "l", col= "dodgerblue")
lines(datainflow$Tahun,datainflow$Riau,col="red")
legend("top",c("Inflow Sumatera Selatan","Inflow Riau"),fill=c("dodgerblue","red"))
2. Komparasi Visualisasi Prediksi Data Outflow Uang Kartal antara Sumatera Selatan dengan Riau Setiap Periode
plot(dataoutflow$Tahun,dataoutflow$`Sumatera Selatan`,type = "l", col= "green")
lines(dataoutflow$Tahun,dataoutflow$Riau,col="mediumorchid")
legend("top",c("Outflow Sumatera Selatan","Outflow Riau"),fill=c("green","mediumorchid"))
3. Komparasi Visualisasi Prediksi Data Inflow-Outflow Uang Kartal antara Sumatera Selatan dengan Riau Setiap Periode
plot(datainflow$Tahun,datainflow$`Sumatera Selatan`,type = "l", col= "dodgerblue")
lines(datainflow$Tahun,datainflow$Riau,col="red")
lines(dataoutflow$Tahun,dataoutflow$`Sumatera Selatan`, col= "green")
lines(dataoutflow$Tahun,dataoutflow$Riau,col="mediumorchid")
legend("top",c("Inflow Sumatera Selatan","Inflow Riau","Outflow Sumatera Selatan","Outflow Riau"),fill=c("dodgerblue","red","green","mediumorchid"))
4. Komparasi Visualisasi Prediksi Data Inflow-Outflow Uang Kartal antara Sumatera Selatan dengan Riau Setiap Bulan
library(readxl)
datainflowperbulan <- read_excel(path = "datainperbulan.xlsx")
dataoutflowperbulan <- read_excel(path = "dataoutperbulan.xlsx")
datainflowperbulan## # A tibble: 128 x 41
## 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 35 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## # Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # Kep. Bangka Belitung <dbl>, DKI Jakarta <dbl>, Jawa <dbl>,
## # Jawa Barat <dbl>, Jawa Tengah <dbl>, Yogyakarta <dbl>, Jawa Timur <dbl>,
## # Banten <dbl>, Bali Nusra <dbl>, Bali <dbl>, Nusa Tenggara Barat <dbl>,
## # Nusa Tenggara Timur <dbl>, Kalimantan <dbl>, Kalimantan Barat <dbl>,
## # Kalimantan Tengah <dbl>, Kalimantan Selatan <dbl>, ...dataoutflowperbulan## # A tibble: 128 x 41
## 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 35 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## # Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # Kep. Bangka Belitung <dbl>, DKI Jakarta <dbl>, Jawa <dbl>,
## # Jawa Barat <dbl>, Jawa Tengah <dbl>, Yogyakarta <dbl>, Jawa Timur <dbl>,
## # Banten <dbl>, Bali Nusra <dbl>, Bali <dbl>, Nusa Tenggara Barat <dbl>,
## # Nusa Tenggara Timur <dbl>, Kalimantan <dbl>, Kalimantan Barat <dbl>,
## # Kalimantan Tengah <dbl>, Kalimantan Selatan <dbl>, ...plot(datainflowperbulan$`Sumatera Selatan`, type = "l", col = "tomato")
lines(datainflowperbulan$Riau,col="darkorchid")
lines(dataoutflowperbulan$`Sumatera Selatan`, col = "green")
lines(dataoutflowperbulan$Riau,col="purple")
legend("top",c("Inflow Sumatera Selatan","Inflow Riau","Outflow Sumatera Selatan","Outflow Riau"),fill=c("tomato","darkorchid","green","purple"))
SumateraSelatantimeseries <- datainflowperbulan$`Sumatera Selatan`
Riautimeseries <- datainflowperbulan$Riau
plot.ts(SumateraSelatantimeseries , type = "l", col = "cyan")
lines(Riautimeseries , type = "l", col = "darkorchid")
legend("top",c("Sumatera Selatan Timeseries","Riau Timeseries"),fill=c("cyan","darkorchid"))
logSumateraSelatan <- log(datainflowperbulan$`Sumatera Selatan`)
logRiau <- log(datainflowperbulan$`Riau`)
plot.ts(logSumateraSelatan, type = "l", col = "peachpuff")
lines(logRiau , type = "l", col = "gold")
legend("top",c("logSumateraSelatan","logRiau"),fill=c("peachpuff","gold"))
library(TTR)## Warning: package 'TTR' was built under R version 4.1.2SumateraSelatanSMA3 <- SMA(datainflowperbulan$`Sumatera Selatan`,n=3)
RiauSMA3 <- SMA(datainflowperbulan$`Riau`,n=3)
plot.ts(SumateraSelatanSMA3, type = "l", col = "black")
lines(RiauSMA3, type = "l", col = "red")
legend("top",c("SumateraSelatanSMA3","RiauSMA3"),fill=c("black","red"))
library(TTR)
SumateraSelatanSMA3 <- SMA(datainflowperbulan$`Sumatera Selatan`,n=8)
RiauSMA3 <- SMA(datainflowperbulan$`Riau`,n=8)
plot.ts(SumateraSelatanSMA3, type = "l", col = "black")
lines(RiauSMA3, type = "l", col = "red")
legend("top",c("SumateraSelatanSMA3","RiauSMA3"),fill=c("black","red"))
5. Komparasi Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal antara Sumatera Selatan dengan Riau
SumateraSelataninflowtimeseries <- ts(datainflowperbulan$`Sulawesi Selatan`, frequency=12, start=c(2011,1))
Riauinflowtimeseries <- ts(datainflowperbulan$Riau, frequency=12, start=c(2011,1))
SumateraSelataninflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 737.2701 681.9942 910.0269 634.0787 697.3718 768.1180 742.7741
## 2012 1825.5671 1188.5240 857.6867 740.7358 1106.8802 906.3162 1168.7513
## 2013 2351.4793 1368.2980 1094.8123 1243.7820 1322.5633 1087.1937 1253.6472
## 2014 2732.2070 1503.5450 1039.9472 1315.0485 1264.3109 1572.9139 1109.0895
## 2015 3352.9496 1704.0220 1150.3860 1310.9590 1026.3442 1495.2299 2945.2499
## 2016 3002.2932 2041.5394 1291.9515 1111.7549 1267.5116 1264.3756 3935.5280
## 2017 2902.7937 1344.6431 876.9484 1336.9740 1605.1893 581.3572 3638.3386
## 2018 3539.6905 1393.2126 874.1457 1285.0432 1451.2186 3097.2785 2757.6692
## 2019 3493.4415 1808.0409 1188.0854 1464.0796 1705.3327 4474.1179 2360.2352
## 2020 4119.1319 2311.2332 1720.2440 809.5786 1135.8491 2606.6298 1359.0222
## 2021 4336.9419 2411.6856 1839.7368 1679.5252 2757.1762 2221.8949 1541.3612
## Aug Sep Oct Nov Dec
## 2011 647.3934 2324.1407 832.8167 1026.7175 590.7962
## 2012 1699.7772 1068.5728 988.4976 1354.5523 795.6488
## 2013 3010.8676 959.0225 1722.4662 1467.0511 888.6079
## 2014 3359.5185 1096.1824 1921.2403 1384.3146 1086.1001
## 2015 1477.0931 1288.5123 1604.9752 1340.7632 886.6034
## 2016 1231.0532 1397.8638 1635.0335 1628.3034 1236.0943
## 2017 1290.0620 1258.9023 1858.8599 1402.9977 706.0825
## 2018 1147.0689 1380.6095 2200.5911 1950.7287 816.4931
## 2019 1448.0190 1832.4393 2337.3045 1729.5425 908.7993
## 2020 1327.9169 1718.9034 1249.1544 2010.3617 1182.8652
## 2021 1547.1478Riauinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 94.24460 96.39424 287.98845 160.06180 194.70583 100.67608
## 2012 445.71970 364.44861 274.48827 235.70588 341.36393 250.99083
## 2013 1548.75771 724.83408 666.22356 1146.69694 714.10313 628.70916
## 2014 897.55475 597.76572 391.46587 414.92963 399.11419 324.09467
## 2015 1095.88812 347.44105 369.02908 424.74718 505.67346 498.57889
## 2016 1332.16109 622.76483 564.49565 377.26617 501.64829 415.02464
## 2017 1228.76098 692.52354 787.21834 671.46804 700.20181 173.00907
## 2018 1545.34390 887.66466 697.71403 627.84201 422.92181 1972.65304
## 2019 1663.41486 723.68853 671.06970 670.02297 372.20685 2633.04629
## 2020 1566.80990 900.25231 656.60197 465.35740 832.48125 1646.18946
## 2021 2241.25936 910.24470 683.86349 608.93339 1522.46355 829.78643
## Jul Aug Sep Oct Nov Dec
## 2011 143.32160 134.02960 1013.73676 341.22178 285.25779 160.83875
## 2012 390.91878 802.77936 408.83238 299.94057 391.02488 241.07860
## 2013 666.15895 1389.62436 454.88185 526.87296 302.26685 164.31963
## 2014 230.89241 1726.82385 377.03621 427.15336 334.94644 236.43117
## 2015 1399.11338 924.21942 357.65246 492.53688 457.74194 283.85194
## 2016 1858.40120 454.01158 563.71821 617.78181 426.00867 477.63763
## 2017 2114.71229 662.80534 502.47310 396.17308 428.57649 195.45782
## 2018 1293.01149 794.86546 685.77238 761.58086 774.35900 265.80837
## 2019 792.15569 841.10671 817.22178 825.61507 713.15676 192.69741
## 2020 754.19735 643.18320 372.80961 524.47867 611.53183 174.17311
## 2021 454.26751 518.24240SumateraSelatanoutflowtimeseries <- ts(dataoutflowperbulan$`Sulawesi Selatan`, frequency=12, start=c(2011,1))
Riauoutflowtimeseries <- ts(dataoutflowperbulan$Riau, frequency=12, start=c(2011,1))
SumateraSelatanoutflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 308.8302 237.8154 704.9791 738.6864 573.7936 593.2387 697.0496
## 2012 510.4450 454.6563 931.5922 1184.4716 912.5994 1076.4658 1011.8941
## 2013 154.0943 456.5932 538.6588 450.5097 726.9099 762.7118 1715.5574
## 2014 571.5631 669.3362 1082.1114 1236.1172 1142.5821 978.4584 3281.9684
## 2015 449.8504 624.7593 1184.9137 1558.5959 1136.3951 1374.2081 3073.0906
## 2016 343.9866 641.9787 824.1808 1442.7011 1391.4076 3169.4211 1467.6018
## 2017 488.8822 582.9862 1170.1978 862.8797 878.1953 3410.4420 721.6211
## 2018 163.1490 578.0414 1446.4317 1365.6673 1504.9645 3092.2174 880.0667
## 2019 343.7722 509.6206 1602.1095 2025.2509 4346.8133 141.9142 1323.0795
## 2020 414.5397 1016.5017 1727.2575 1794.6297 3671.4842 634.0485 1261.8806
## 2021 237.7465 809.6438 1897.2916 2804.0416 2712.6154 972.5763 1553.6345
## Aug Sep Oct Nov Dec
## 2011 2067.3531 489.8115 724.9256 731.3198 1099.4002
## 2012 1894.6604 681.6072 968.0655 616.1420 1630.0031
## 2013 975.9749 1480.8559 1165.7191 1060.4188 1996.6188
## 2014 833.3638 1668.2002 909.6691 1107.7162 2163.5076
## 2015 1402.7793 1483.1910 848.9114 1283.1252 1815.9132
## 2016 1009.3475 1233.0616 893.9218 1163.5838 1912.4182
## 2017 1746.5581 1122.7270 800.9915 1375.4004 1997.9313
## 2018 1660.2147 1270.9735 1219.2608 1317.6617 2280.1121
## 2019 1607.7889 1145.5304 909.9568 1620.9901 2511.9732
## 2020 1815.4617 1463.4415 2319.1946 1609.1700 2775.8378
## 2021 1029.6811Riauoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 478.18402 400.24595 621.35321 1005.56107 1000.35374 1365.96130
## 2012 292.47450 399.76750 880.86006 1049.68113 1055.29479 1142.69911
## 2013 116.34632 569.05345 2345.35727 412.85210 1045.96329 1004.92649
## 2014 517.96101 526.24079 1089.97967 1000.53879 1182.86056 1199.39334
## 2015 133.58209 757.00411 1048.19275 1317.24918 1173.47065 1965.00327
## 2016 264.81101 670.51938 998.35476 1250.91662 1523.48445 4170.88866
## 2017 733.56292 981.17365 1359.41399 1239.79585 1413.94085 3856.69476
## 2018 233.11415 1118.03060 1545.86969 1215.64481 2476.59753 3343.03974
## 2019 455.48443 1012.74002 1340.33344 1521.82191 4902.80531 241.49091
## 2020 739.71921 831.87016 1264.41224 1774.60350 2925.82841 282.77052
## 2021 311.09352 805.14586 1430.24476 2632.46893 3111.28761 1073.67143
## Jul Aug Sep Oct Nov Dec
## 2011 815.43379 2729.10217 154.42178 829.93388 873.64100 2159.95096
## 2012 1196.25287 2392.32861 381.04524 883.96286 968.57206 2370.85940
## 2013 1473.20994 1758.54800 892.49248 1341.31082 1558.92781 2941.37515
## 2014 3974.55298 13.89336 971.59826 969.79530 1076.07146 2634.65301
## 2015 3286.54673 393.89838 718.78270 935.00142 1054.45513 3005.38270
## 2016 515.04790 1100.53865 1629.71683 1273.01584 1438.08721 2809.65000
## 2017 330.25241 1530.30977 896.72821 1317.25781 1705.10587 2763.50350
## 2018 735.25593 1364.76585 955.53100 1303.13335 1240.43316 2394.18052
## 2019 1223.33771 1452.78989 1124.43995 1242.01385 1649.73723 3110.25361
## 2020 1530.19271 1470.10144 1394.12769 2017.60832 1409.04284 3498.29809
## 2021 1692.92089 1573.91533plot.ts(SumateraSelataninflowtimeseries,type = "l", col = "khaki")
lines(Riauinflowtimeseries, type = "l", col = "sienna")
legend("top",c("SumateraSelataninflowtimeseries","Riauinflowtimeseries"),fill=c("khaki","sienna"))
plot.ts(SumateraSelatanoutflowtimeseries,type = "l", col = "khaki")
lines(Riauoutflowtimeseries, type = "l", col = "sienna")
legend("top",c("SumateraSelatanoutflowtimeseries","Riauoutflowtimeseries"),fill=c("khaki","sienna"))
SumateraSelatanintimeseriescomponents <- decompose(SumateraSelataninflowtimeseries)
Riauintimeseriescomponents <- decompose(Riauinflowtimeseries)
SumateraSelatanintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2012 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2013 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2014 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2015 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2016 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2017 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2018 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2019 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2020 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2021 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## Jul Aug Sep Oct Nov Dec
## 2011 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2012 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2013 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2014 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2015 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2016 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2017 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2018 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2019 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2020 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2021 535.054604 49.695394Riauintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2012 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2013 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2014 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2015 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2016 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2017 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2018 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2019 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2020 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2021 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## Jul Aug Sep Oct Nov Dec
## 2011 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2012 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2013 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2014 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2015 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2016 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2017 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2018 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2019 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2020 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2021 306.31477 167.03441SumateraSelatanouttimeseriescomponents <- decompose(SumateraSelatanoutflowtimeseries)
Riauouttimeseriescomponents <- decompose(Riauoutflowtimeseries)
SumateraSelatanouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2012 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2013 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2014 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2015 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2016 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2017 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2018 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2019 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2020 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2021 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## Jul Aug Sep Oct Nov Dec
## 2011 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2012 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2013 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2014 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2015 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2016 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2017 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2018 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2019 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2020 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2021 290.34538 246.22820Riauouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2012 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2013 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2014 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2015 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2016 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2017 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2018 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2019 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2020 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2021 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## Jul Aug Sep Oct Nov Dec
## 2011 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2012 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2013 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2014 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2015 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2016 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2017 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2018 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2019 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2020 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2021 140.29335 51.92179plot(SumateraSelatanintimeseriescomponents$seasonal,type = "l", col = "purple")
lines(Riauintimeseriescomponents$seasonal,col="palegreen")
lines(SumateraSelatanouttimeseriescomponents$seasonal, type = "l", col = "lightskyblue")
lines(Riauouttimeseriescomponents$seasonal,col="orange")
legend("top",c("Sumatera Selatan Inflow","Riau Inflow", "Sumatera Selatan Outflow","Riau Outflow"),fill=c("purple","palegreen","lightskyblue","orange"))
plot(SumateraSelatanintimeseriescomponents$trend,type = "l", col = "purple")
lines(Riauintimeseriescomponents$trend,col="palegreen")
lines(SumateraSelatanouttimeseriescomponents$trend, type = "l", col = "lightskyblue")
lines(Riauouttimeseriescomponents$trend,col="orange")
legend("top",c("Sumatera Selatan Inflow","Riau Inflow", "Sumatera Selatan Outflow","Riau Outflow"),fill=c("purple","palegreen","lightskyblue","orange"))
plot(SumateraSelatanintimeseriescomponents$random,type = "l", col = "purple")
lines(Riauintimeseriescomponents$random,col="palegreen")
lines(SumateraSelatanouttimeseriescomponents$random, type = "l", col = "lightskyblue")
lines(Riauouttimeseriescomponents$random,col="orange")
legend("top",c("Sumatera Selatan Inflow","Riau Inflow", "Sumatera Selatan Outflow","Riau Outflow"),fill=c("purple","palegreen","lightskyblue","orange"))
plot(SumateraSelatanintimeseriescomponents$figure,type = "l", col = "purple")
lines(Riauintimeseriescomponents$figure,col="palegreen")
lines(SumateraSelatanouttimeseriescomponents$figure, type = "l", col = "lightskyblue")
lines(Riauouttimeseriescomponents$figure,col="orange")
legend("top",c("Sumatera Selatan Inflow","Riau Inflow", "Sumatera Selatan Outflow","Riau Outflow"),fill=c("purple","palegreen","lightskyblue","orange"))