Komparasi Prediksi dan Visualisasi Inflow-Outflow pada Uang Kartal antara Riau dan Kepulauan Riau
Hamidah Lutfiyanti Maharani
2/25/2022
Dosen Pengampu : Prof. Dr. Suhartono, M.Kom
Mata Kuliah : Linear Algebra
Prodi : Teknik Informatika
Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang”
Pengertian Inflow Outflow Uang Kartal
Inflow merupakan masuknya sejumlah dana luar negeri kedalam suatu negara untuk tujuan investasi.
Outflow merupakan transaksi pembelian asset dari luar negeri. Pembelian asset negara asing akan mengeluarkan dana untuk membayar pembelian asset tersebut.
Berikut ini contoh penerapan komparasi visualisasi prediksi data Inflow-Outflow Uang Kartal antara Riau dan Kepulauan Riau menggunakan bahasa pemograman R.
library(readxl)
datainflow <- read_excel(path = "C:/Users/DELL LATITUDE 7280/Documents/KULIAH/SEMESTER 2/LINEAR ALGEBRA/Inflow Outflow/datainflow.xlsx")
datainflow## # A tibble: 10 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.
## # ... 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/DELL LATITUDE 7280/Documents/KULIAH/SEMESTER 2/LINEAR ALGEBRA/Inflow Outflow/dataoutflow.xlsx")
dataoutflow## # A tibble: 10 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.
## # ... with 5 more variables: Jambi <dbl>, `Sumatera Selatan` <dbl>,
## # Bengkulu <dbl>, Lampung <dbl>, `Kep. Bangka Belitung` <dbl>1. Komparasi Visualisasi dan Prediksi Data Inflow Uang Kartal antara Riau dengan Kepulauan Riau Setiap Periode
plot(datainflow$Tahun,datainflow$'Riau',type = "l", col= "cornsilk4")
lines(datainflow$Tahun,datainflow$'Kep. Riau',col="deepskyblue4")
legend("top",c("Inflow Riau","Inflow Kepulauan Riau"),fill=c("cornsilk4","deepskyblue4"))
2. Komparasi Visualisasi dan Prediksi Data Outflow Uang Kartal antara Riau dengan Kepulauan Riau Setiap Periode
plot(dataoutflow$Tahun,dataoutflow$'Riau',type = "l", col= "darkseagreen")
lines(dataoutflow$Tahun,dataoutflow$'Kep. Riau',col="darkkhaki")
legend("top",c("Outflow Riau","Outflow Kepulauan Riau"),fill=c("darkseagreen","darkkhaki"))
3. Komparasi Visualisasi dan Prediksi Data Inflow-Outflow Uang Kartal antara Riau dengan Kepulauan Riau Setiap Periode
plot(datainflow$Tahun,datainflow$'Riau',type = "l", col= "cornsilk4")
lines(datainflow$Tahun,datainflow$'Kep. Riau',col="deepskyblue4")
lines(dataoutflow$Tahun,dataoutflow$'Riau',col= "darkseagreen")
lines(dataoutflow$Tahun,dataoutflow$'Kep. Riau',col="darkkhaki")
legend("top",c("Inflow Riau","Inflow Kepulauan Riau","Outflow Riau","Outflow Kepulauan Riau"),fill=c("cornsilk4","deepskyblue4","darkseagreen","darkkhaki"))
4. Komparasi Visualisasi dan Prediksi Data Inflow-Outflow Uang Kartal antara Riau dengan Kepulauan Riau Setiap Bulan
library(readxl)
datainflowperbulan <- read_excel(path = "C:/Users/DELL LATITUDE 7280/Documents/KULIAH/SEMESTER 2/LINEAR ALGEBRA/Inflow Outflow/inflowbulanan.xlsx")## New names:
## * `` -> ...2dataoutflowperbulan <- read_excel(path = "C:/Users/DELL LATITUDE 7280/Documents/KULIAH/SEMESTER 2/LINEAR ALGEBRA/Inflow Outflow/outflowbulanan.xlsx")## New names:
## * `` -> ...2datainflowperbulan## # A tibble: 128 x 13
## Bulan ...2 Sumatera Aceh `Sumatera Utara` `Sumatera Barat`
## <dttm> <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 NA 4164. 124. 2068. 545.
## 2 2011-02-01 00:00:00 NA 3338. 115. 1826. 450.
## 3 2011-03-01 00:00:00 NA 4878. 154. 2028. 849.
## 4 2011-04-01 00:00:00 NA 3157. 122. 1429. 539.
## 5 2011-05-01 00:00:00 NA 3821. 123. 1539. 692.
## 6 2011-06-01 00:00:00 NA 3686. 151. 1637. 592.
## 7 2011-07-01 00:00:00 NA 4370. 107. 1791. 800.
## 8 2011-08-01 00:00:00 NA 3668. 184. 1256. 586.
## 9 2011-09-01 00:00:00 NA 12875. 606. 4172. 2176.
## 10 2011-10-01 00:00:00 NA 4777. 158. 1941. 787.
## # ... with 118 more rows, and 7 more variables: Riau <dbl>, `Kep. Riau` <dbl>,
## # Jambi <dbl>, `Sumatera Selatan` <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # `Kep. Bangka Belitung` <dbl>dataoutflowperbulan## # A tibble: 128 x 13
## Bulan ...2 Sumatera Aceh `Sumatera Utara` `Sumatera Barat`
## <dttm> <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 NA 3442. 350. 941. 307.
## 2 2011-02-01 00:00:00 NA 3989. 193. 990. 228.
## 3 2011-03-01 00:00:00 NA 4229. 230. 1209. 347.
## 4 2011-04-01 00:00:00 NA 6721. 529. 1653. 336.
## 5 2011-05-01 00:00:00 NA 5787. 523. 1465. 328.
## 6 2011-06-01 00:00:00 NA 7395. 406. 2167. 399.
## 7 2011-07-01 00:00:00 NA 7154. 958. 1695. 449.
## 8 2011-08-01 00:00:00 NA 16043. 1046. 4104. 1376.
## 9 2011-09-01 00:00:00 NA 1915. 124. 824. 148.
## 10 2011-10-01 00:00:00 NA 5174. 634. 1392. 299.
## # ... with 118 more rows, and 7 more variables: Riau <dbl>, `Kep. Riau` <dbl>,
## # Jambi <dbl>, `Sumatera Selatan` <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # `Kep. Bangka Belitung` <dbl>plot(datainflowperbulan$'Riau', type = "l", col = "darksalmon")
lines(datainflowperbulan$'Kep. Riau',col="darkseagreen3")
lines(dataoutflowperbulan$'Riau', col = "darkslategray2")
lines(dataoutflowperbulan$'Kep. Riau',col="grey")
legend("top",c("Inflow Riau","Inflow Kepulauan Riau","Outflow Riau","Outflow Kepulauan Riau"),fill=c("darksalmon","darkseagreen3","darkslategray2","grey"))
Riautimeseries <- datainflowperbulan$'Riau'
KepulauanRiautimeseries <- datainflowperbulan$'Kep. Riau'
plot.ts(Riautimeseries , type = "l", col = "coral")
lines(KepulauanRiautimeseries , type = "l", col = "brown")
legend("top",c("Riau Timeseries","Kepuluan Riau Timeseries"),fill=c("coral","brown"))
logRiau <- log(datainflowperbulan$'Riau')
logKepulauanRiau <- log(datainflowperbulan$'Kep. Riau')
plot.ts(logRiau, type = "l", col = "black")
lines(logKepulauanRiau , type = "l", col = "coral")
legend("top",c("log Riau","log Kepulauan Riau"),fill=c("black","brown"))
library(TTR)
RiauSMA3 <- SMA(datainflowperbulan$'Riau',n=3)
KepulauanRiauSMA3 <- SMA(datainflowperbulan$'Kep. Riau',n=3)
plot.ts(RiauSMA3, type = "l", col = "coral")
lines(KepulauanRiauSMA3, type = "l", col = "brown")
legend("top",c("Riau SMA3","Kepulauan Riau SMA3"),fill=c("coral","brown"))
library(TTR)
RiauSMA3 <- SMA(datainflowperbulan$'Riau',n=8)
KepulauanRiauSMA3 <- SMA(datainflowperbulan$'Kep. Riau',n=8)
plot.ts(RiauSMA3, type = "l", col = "coral")
lines(KepulauanRiauSMA3, type = "l", col = "brown")
legend("top",c("RiauSMA3","Kepulauan RiauSMA3"),fill=c("coral","brown"))
5. Komparasi Visualisasi dan Prediksi Data Inflow-Outflow Time Series Uang Kartal antara Riau dengan Kepulauan Riau
Riauinflowtimeseries <- ts(datainflowperbulan$'Riau', frequency=12, start=c(2011,1))
KepulauanRiauinflowtimeseries <- ts(datainflowperbulan$'Kep. Riau', frequency=12, start=c(2011,1))
Riauinflowtimeseries## 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 518.24240KepulauanRiauinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 84.22317 45.28489 87.19606 106.27655 79.41735 79.39071
## 2012 154.12964 248.64100 144.87430 208.13217 195.88684 142.58026
## 2013 386.21824 264.78916 225.74983 311.08538 210.63038 202.38804
## 2014 264.22703 270.00068 175.25704 142.22593 123.06405 103.56327
## 2015 527.48615 169.98619 240.82415 193.34540 234.14488 170.06052
## 2016 661.93008 385.82752 312.32158 276.09507 316.70196 150.48089
## 2017 512.43711 385.28011 383.55697 202.89962 208.91189 105.70146
## 2018 711.86420 353.76509 374.70466 387.21015 311.02800 979.33988
## 2019 845.27320 521.35431 474.60558 353.38377 268.14443 1193.95980
## 2020 731.48682 637.43455 386.64090 524.91472 379.63698 793.99943
## 2021 1078.46297 611.51858 423.88140 540.01754 976.15802 569.57964
## Jul Aug Sep Oct Nov Dec
## 2011 120.99479 64.58641 369.70995 126.63637 168.11264 94.51409
## 2012 206.86457 315.89649 216.54585 155.27273 155.62754 91.58926
## 2013 294.45220 919.59500 181.59798 217.10630 110.05512 54.15557
## 2014 60.20677 631.73314 222.13537 258.28860 241.39837 70.90054
## 2015 561.93669 310.80650 164.20381 281.41579 249.20775 114.23718
## 2016 809.08819 262.15868 351.87129 328.02559 261.59397 200.41305
## 2017 839.30770 414.28185 388.93568 378.94493 384.83909 206.46159
## 2018 405.77807 331.14363 383.34951 308.45259 414.23273 172.78907
## 2019 533.39994 388.53399 422.87038 421.73089 397.68404 256.38430
## 2020 507.29098 486.49911 527.42384 414.62940 516.46748 269.03594
## 2021 393.47068 415.69709Riauoutflowtimeseries <- ts(dataoutflowperbulan$'Riau', frequency=12, start=c(2011,1))
KepulauanRiauoutflowtimeseries <- ts(dataoutflowperbulan$'Kep. Riau', frequency=12, start=c(2011,1))
Riauoutflowtimeseries## 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 1573.91533KepulauanRiauoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 189.20654 268.01656 208.80011 364.35734 447.61217 516.05275
## 2012 332.54370 239.53906 479.70454 362.89160 542.67878 658.10047
## 2013 119.26413 365.97218 463.90646 372.88312 673.61694 581.56634
## 2014 517.98070 246.77804 530.04786 715.09716 830.04557 997.34576
## 2015 192.79623 628.08355 542.19874 855.97355 724.82924 1138.74670
## 2016 256.75804 506.42349 672.97048 840.07221 983.30103 1966.97714
## 2017 410.59624 367.54302 749.04887 703.31521 964.80569 2092.64435
## 2018 229.17137 850.81662 993.83877 936.80576 1739.35274 1649.76547
## 2019 351.32570 533.80541 1070.37711 1147.55958 2819.62986 249.37983
## 2020 627.16179 494.16093 823.30668 707.72583 963.88952 220.66070
## 2021 140.35818 543.53780 588.91635 1222.73228 1161.79670 437.98386
## Jul Aug Sep Oct Nov Dec
## 2011 584.09410 1311.58555 99.21788 270.28783 510.72809 1048.66737
## 2012 660.22824 1072.58101 276.95017 630.29531 519.38376 1190.73164
## 2013 1117.71986 754.58448 735.90065 919.20095 866.05783 1776.70961
## 2014 2056.31540 207.71173 816.86614 1059.52199 601.55529 1542.99310
## 2015 1695.67523 534.01001 678.06544 545.91971 784.86345 1481.35677
## 2016 604.40807 711.35343 871.77960 638.81776 828.88407 1185.88923
## 2017 461.27959 1028.34957 764.82488 906.81927 1121.10595 1179.11994
## 2018 941.81987 1152.64514 825.48506 895.10397 878.93357 1503.35024
## 2019 971.67306 1124.34920 811.30642 969.06768 1018.72975 1576.51468
## 2020 615.39884 525.84998 521.60564 967.01965 506.01366 1488.62254
## 2021 611.98142 420.24414plot.ts(Riauinflowtimeseries,type = "l", col = "darkgoldenrod")
lines(KepulauanRiauinflowtimeseries, type = "l", col = "cornflowerblue")
legend("top",c("Riauinflowtimeseries","KepulauanRiauinflowtimeseries"),fill=c("darkgoldenrod","cornflowerblue"))
plot.ts(Riauoutflowtimeseries,type = "l", col = "cadetblue")
lines(KepulauanRiauoutflowtimeseries, type = "l", col = "darkgrey")
legend("top",c("Riauoutflowtimeseries","KepulauanRiauoutflowtimeseries"),fill=c("cadetblue","darkgrey"))
Riauintimeseriescomponents <- decompose(Riauinflowtimeseries)
KepulauanRiauintimeseriescomponents <- decompose(KepulauanRiauinflowtimeseries)
Riauintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2012 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2013 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2014 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2015 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2016 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2017 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2018 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2019 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2020 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2021 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## Jul Aug Sep Oct Nov Dec
## 2011 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2012 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2013 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2014 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2015 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2016 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2017 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2018 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2019 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2020 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2021 306.31477 167.03441KepulauanRiauintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2012 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2013 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2014 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2015 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2016 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2017 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2018 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2019 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2020 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2021 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## Jul Aug Sep Oct Nov Dec
## 2011 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2012 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2013 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2014 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2015 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2016 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2017 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2018 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2019 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2020 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2021 104.58593 76.67545Riauouttimeseriescomponents <- decompose(Riauoutflowtimeseries)
KepulauanRiauouttimeseriescomponents <- decompose(KepulauanRiauoutflowtimeseries)
Riauouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2012 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2013 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2014 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2015 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2016 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2017 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2018 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2019 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2020 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2021 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## Jul Aug Sep Oct Nov Dec
## 2011 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2012 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2013 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2014 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2015 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2016 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2017 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2018 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2019 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2020 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2021 140.29335 51.92179KepulauanRiauouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2012 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2013 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2014 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2015 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2016 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2017 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2018 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2019 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2020 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2021 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## Jul Aug Sep Oct Nov Dec
## 2011 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2012 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2013 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2014 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2015 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2016 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2017 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2018 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2019 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2020 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2021 167.80534 38.30166plot(Riauintimeseriescomponents$seasonal,type = "l", col = "antiquewhite4")
lines(KepulauanRiauintimeseriescomponents$seasonal,col="aquamarine3")
lines(Riauouttimeseriescomponents$seasonal, type = "l", col = "purple")
lines(KepulauanRiauouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Riau Inflow","Kepulauan Riau Inflow", "Riau Outflow","Kepulauan Riau Outflow"),fill=c("antiquewhite4","aquamarine3","purple","blue"))
plot(Riauintimeseriescomponents$trend,type = "l", col = "black")
lines(KepulauanRiauintimeseriescomponents$trend,col="brown")
lines(Riauouttimeseriescomponents$trend, type = "l", col = "purple")
lines(KepulauanRiauouttimeseriescomponents$trend,col="blue")
legend("top",c("Riau Inflow","Kepulauan Riau Inflow", "Riau Outflow","Kepulauan Riau Outflow"),fill=c("black","brown","purple","blue"))
plot(Riauintimeseriescomponents$random,type = "l", col = "black")
lines(KepulauanRiauintimeseriescomponents$random,col="brown")
lines(Riauouttimeseriescomponents$random, type = "l", col = "purple")
lines(KepulauanRiauouttimeseriescomponents$random,col="blue")
legend("top",c("Riau Inflow","KepulauanRiau Inflow", "Riau Outflow","KepulauanRiau Outflow"),fill=c("black","brown","purple","blue"))
plot(Riauintimeseriescomponents$figure,type = "l", col = "black")
lines(KepulauanRiauintimeseriescomponents$figure,col="brown")
lines(Riauouttimeseriescomponents$figure, type = "l", col = "purple")
lines(KepulauanRiauouttimeseriescomponents$figure,col="blue")
legend("top",c("Riau Inflow","KepulauanRiau Inflow", "Riau Outflow","KepulauanRiau Outflow"),fill=c("black","brown","purple","blue"))