Komparasi Visualisasi dan Prediksi Data Inflow-Outflow Uang Kartal antara Jambi dengan Aceh
Adila Qurrota A’yun
2/21/2022
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 adalah uang yang masuk ke Bank Indonesia melalui kegiatan penyetoran. Sedangkan outflow 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 dan prediksi data inflow-outflow uang kartal antara Jambi dengan Aceh menggunakan bahasa pemrograman R.
library(readxl)
datainflow <- read_excel(path ="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 ="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 Jambi dengan Aceh Setiap Periode
plot(datainflow$Tahun,datainflow$Jambi,type = "l", col= "black")
lines(datainflow$Tahun,datainflow$Aceh,col="brown")
legend("top",c("Inflow Jambi","Inflow Aceh"),fill=c("black","brown"))
2. Komparasi Visualisasi dan Prediksi Data Outflow Uang Kartal antara Jambi dengan Aceh Setiap Periode
plot(dataoutflow$Tahun,dataoutflow$Jambi,type = "l", col= "purple")
lines(dataoutflow$Tahun,dataoutflow$Aceh,col="blue")
legend("top",c("Outflow Jambi","Outflow Aceh"),fill=c("purple","blue"))
3. Komparasi Visualisasi dan Prediksi Data Inflow-Outflow Uang Kartal antara Jambi dengan Aceh Setiap Periode
plot(datainflow$Tahun,datainflow$Jambi,type = "l", col= "black")
lines(datainflow$Tahun,datainflow$Aceh,col="brown")
lines(dataoutflow$Tahun,dataoutflow$Jambi,col= "purple")
lines(dataoutflow$Tahun,dataoutflow$Aceh,col="blue")
legend("top",c("Inflow Jambi","Inflow Aceh","Outflow Jambi","Outflow Aceh"),fill=c("black","brown","purple","blue"))
4. Komparasi Visualisasi dan Prediksi Data Inflow-Outflow Uang Kartal antara Jambi dengan Aceh Setiap Bulan
library(readxl)
datainflowperbulan <- read_excel(path = "inflowbulanan.xlsx")## New names:
## * `` -> ...2dataoutflowperbulan <- read_excel(path = "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
## Keterangan ...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$Jambi, type = "l", col = "black")
lines(datainflowperbulan$Aceh,col="brown")
lines(dataoutflowperbulan$Jambi, col = "purple")
lines(dataoutflowperbulan$Aceh,col="blue")
legend("top",c("Inflow Jambi","Inflow Aceh","Outflow Jambi","Outflow Aceh"),fill=c("black","brown","purple","blue"))
Jambitimeseries <- datainflowperbulan$Jambi
Acehtimeseries <- datainflowperbulan$Aceh
plot.ts(Jambitimeseries , type = "l", col = "black")
lines(Acehtimeseries , type = "l", col = "brown")
legend("top",c("Jambi Timeseries","Aceh Timeseries"),fill=c("black","brown"))
logJambi <- log(datainflowperbulan$Jambi)
logAceh <- log(datainflowperbulan$Aceh)
plot.ts(logJambi, type = "l", col = "black")
lines(logAceh , type = "l", col = "brown")
legend("top",c("logJambi","logAceh"),fill=c("black","brown"))
library(TTR)
JambiSMA3 <- SMA(datainflowperbulan$Jambi,n=3)
AcehSMA3 <- SMA(datainflowperbulan$Aceh,n=3)
plot.ts(JambiSMA3, type = "l", col = "black")
lines(AcehSMA3, type = "l", col = "brown")
legend("top",c("JambiSMA3","AcehSMA3"),fill=c("black","brown"))
library(TTR)
JambiSMA3 <- SMA(datainflowperbulan$Jambi,n=8)
AcehSMA3 <- SMA(datainflowperbulan$Aceh,n=8)
plot.ts(JambiSMA3, type = "l", col = "black")
lines(AcehSMA3, type = "l", col = "brown")
legend("top",c("JambiSMA3","AcehSMA3"),fill=c("black","brown"))
5. Komparasi Visualisasi dan Prediksi Data Inflow-Outflow Time Series Uang Kartal antara Jambi dengan Aceh
Jambiinflowtimeseries <- ts(datainflowperbulan$Jambi, frequency=12, start=c(2011,1))
Acehinflowtimeseries <- ts(datainflowperbulan$Aceh, frequency=12, start=c(2011,1))
Jambiinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 48.21238 39.91336 202.77581 76.36759 102.29337 80.38363
## 2012 214.78357 185.06614 118.25569 112.18712 176.73267 131.65442
## 2013 440.25724 250.16557 156.40296 131.70444 80.43460 90.88444
## 2014 648.84622 443.17728 218.60749 372.98546 277.49781 326.07002
## 2015 800.91577 310.67803 334.27000 339.99797 285.21811 266.80514
## 2016 723.86727 399.44327 227.89071 207.32596 294.89205 265.25147
## 2017 436.71704 349.18620 374.44420 291.87853 265.93193 109.35945
## 2018 850.92308 423.79251 432.57396 284.21732 331.44473 943.33760
## 2019 928.32921 508.44605 501.71263 395.87576 375.81227 1377.08370
## 2020 929.25223 453.21208 375.57835 488.00832 366.02264 926.36280
## 2021 1319.31010 533.89020 481.47669 442.30053 954.47189 568.16022
## Jul Aug Sep Oct Nov Dec
## 2011 118.45074 91.88117 618.33464 137.23519 238.83742 112.93547
## 2012 178.67562 446.70847 180.60249 96.89252 190.29249 106.61224
## 2013 150.73569 696.17818 239.01380 381.11280 240.84581 189.04884
## 2014 228.38825 1336.65537 383.31015 366.82210 328.60113 238.13597
## 2015 1033.05014 473.13670 295.54859 329.75416 266.79923 241.96031
## 2016 1069.41796 211.81993 325.26906 251.99989 234.81316 186.17002
## 2017 1008.96424 331.35488 369.25742 288.45059 300.80490 277.28824
## 2018 555.66909 452.09732 390.12811 409.82051 356.98477 225.60052
## 2019 517.64046 582.60662 370.00861 477.26284 302.21112 149.17703
## 2020 418.88012 362.62433 363.94528 290.43227 404.08403 249.99980
## 2021 337.72947 342.40788Acehinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 124.33329 115.14321 154.41614 122.18349 122.75253 151.37534
## 2012 315.65341 292.57807 170.13069 139.33374 167.56600 119.32971
## 2013 5571.15653 3457.27812 3253.57336 3775.08977 3705.38033 3449.77565
## 2014 779.00596 332.03457 248.89939 260.82180 168.17801 194.97802
## 2015 836.57498 376.45107 317.53476 263.06848 256.64615 398.59527
## 2016 883.11220 498.41373 242.45180 218.98473 298.46423 450.32018
## 2017 1120.37553 452.83734 347.32016 240.71874 299.60563 194.84441
## 2018 1279.35872 366.57150 278.86587 262.95066 288.49282 1005.08498
## 2019 1293.88334 565.87121 397.27368 342.84300 420.44274 1554.92585
## 2020 1641.95487 692.74998 297.06861 281.42142 489.21304 1095.11262
## 2021 762.78539 487.91516 368.91965 308.33410 566.54102 502.60975
## Jul Aug Sep Oct Nov Dec
## 2011 107.22432 183.84525 605.62334 157.64630 287.24653 176.19523
## 2012 196.61835 420.06418 286.31394 142.89984 288.58842 80.49051
## 2013 3456.32173 8516.17096 243.91990 379.41362 322.99838 205.46842
## 2014 173.99322 1306.11875 271.45458 454.45573 219.11177 157.53593
## 2015 977.94399 495.56495 179.23767 257.65850 227.20326 123.34945
## 2016 1374.47417 310.75050 538.99459 432.31664 301.61184 225.04199
## 2017 1149.75614 264.01934 627.70230 365.36280 275.68807 175.99260
## 2018 784.64208 369.23511 426.04458 344.08223 243.18631 150.59965
## 2019 473.28934 684.81679 405.51614 467.20195 436.23339 466.53727
## 2020 257.81810 592.86464 410.39330 273.60601 438.09977 170.52803
## 2021 280.32142 424.17610Jambioutflowtimeseries <- ts(dataoutflowperbulan$Jambi, frequency=12, start=c(2011,1))
Acehoutflowtimeseries <- ts(dataoutflowperbulan$Aceh, frequency=12, start=c(2011,1))
Jambioutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 297.46348 280.08970 341.37188 474.26014 371.36905 540.43609
## 2012 133.61579 321.29557 315.41057 373.26078 441.58952 474.63459
## 2013 110.31731 184.50535 223.54744 235.42017 450.54670 349.51626
## 2014 351.35683 459.63127 637.62828 526.41165 683.34064 651.89272
## 2015 249.99472 486.10988 549.06994 721.86428 701.16932 931.14718
## 2016 229.69662 442.46621 487.32817 572.51965 587.13872 1610.89703
## 2017 394.17886 553.63581 500.03923 530.31764 570.86673 1961.91565
## 2018 275.03184 451.87980 498.71186 687.34280 1222.83919 1579.32715
## 2019 218.20233 534.52562 559.51510 895.65817 2018.12386 147.10847
## 2020 230.43948 421.99569 606.04929 713.68012 1262.75583 143.79548
## 2021 54.41456 487.87292 732.48101 1261.14201 1578.66374 642.31328
## Jul Aug Sep Oct Nov Dec
## 2011 428.10203 1056.05643 92.78528 295.39728 272.21261 767.15036
## 2012 330.20592 835.74847 221.85612 472.49384 299.07579 794.04754
## 2013 839.48154 339.88048 732.69193 819.24007 782.02490 1235.18658
## 2014 1929.38736 274.46904 553.86575 703.65271 588.68032 1000.86095
## 2015 1582.71912 395.76377 549.45261 479.75684 631.21748 1046.24662
## 2016 456.38157 430.25770 842.64910 521.69293 648.58138 944.35648
## 2017 212.49734 680.41258 470.55865 568.53590 820.95090 1169.98413
## 2018 391.43773 555.29629 475.32140 545.11918 735.03562 1042.05433
## 2019 717.81375 656.73797 617.28665 719.15618 727.75492 1392.15834
## 2020 633.64958 610.36918 689.06184 1124.09728 807.10093 1706.97368
## 2021 664.55917 624.91746Acehoutflowtimeseries## 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 320.71844plot.ts(Jambiinflowtimeseries,type = "l", col = "black")
lines(Acehinflowtimeseries, type = "l", col = "brown")
legend("top",c("Jambiinflowtimeseries","Acehinflowtimeseries"),fill=c("black","brown"))
plot.ts(Jambioutflowtimeseries,type = "l", col = "purple")
lines(Acehoutflowtimeseries, type = "l", col = "blue")
legend("top",c("Jambioutflowtimeseries","Acehoutflowtimeseries"),fill=c("purple","blue"))
Jambiintimeseriescomponents <- decompose(Jambiinflowtimeseries)
Acehintimeseriescomponents <- decompose(Acehinflowtimeseries)
Jambiintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2012 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2013 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2014 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2015 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2016 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2017 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2018 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2019 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2020 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2021 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## Jul Aug Sep Oct Nov Dec
## 2011 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2012 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2013 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2014 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2015 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2016 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2017 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2018 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2019 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2020 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2021 156.97755 120.14214Acehintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2012 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2013 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2014 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2015 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2016 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2017 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2018 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2019 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2020 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2021 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## Jul Aug Sep Oct Nov Dec
## 2011 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2012 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2013 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2014 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2015 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2016 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2017 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2018 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2019 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2020 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2021 209.38959 624.31306Jambiouttimeseriescomponents <- decompose(Jambioutflowtimeseries)
Acehouttimeseriescomponents <- decompose(Acehoutflowtimeseries)
Jambiouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2012 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2013 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2014 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2015 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2016 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2017 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2018 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2019 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2020 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2021 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## Jul Aug Sep Oct Nov Dec
## 2011 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2012 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2013 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2014 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2015 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2016 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2017 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2018 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2019 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2020 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2021 120.11152 -48.40994Acehouttimeseriescomponents$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(Jambiintimeseriescomponents$seasonal,type = "l", col = "black")
lines(Acehintimeseriescomponents$seasonal,col="brown")
lines(Jambiouttimeseriescomponents$seasonal, type = "l", col = "purple")
lines(Acehouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Jambi Inflow","Aceh Inflow", "Jambi Outflow","Aceh Outflow"),fill=c("black","brown","purple","blue"))
plot(Jambiintimeseriescomponents$trend,type = "l", col = "black")
lines(Acehintimeseriescomponents$trend,col="brown")
lines(Jambiouttimeseriescomponents$trend, type = "l", col = "purple")
lines(Acehouttimeseriescomponents$trend,col="blue")
legend("top",c("Jambi Inflow","Aceh Inflow", "Jambi Outflow","Aceh Outflow"),fill=c("black","brown","purple","blue"))
plot(Jambiintimeseriescomponents$random,type = "l", col = "black")
lines(Acehintimeseriescomponents$random,col="brown")
lines(Jambiouttimeseriescomponents$random, type = "l", col = "purple")
lines(Acehouttimeseriescomponents$random,col="blue")
legend("top",c("Jambi Inflow","Aceh Inflow", "Jambi Outflow","Aceh Outflow"),fill=c("black","brown","purple","blue"))
plot(Jambiintimeseriescomponents$figure,type = "l", col = "black")
lines(Acehintimeseriescomponents$figure,col="brown")
lines(Jambiouttimeseriescomponents$figure, type = "l", col = "purple")
lines(Acehouttimeseriescomponents$figure,col="blue")
legend("top",c("Jambi Inflow","Aceh Inflow", "Jambi Outflow","Aceh Outflow"),fill=c("black","brown","purple","blue"))