Komparasi Visualisasi dan Prediksi Data Inflow-Outflow Uang Kartal antara Lampung dengan Bengkulu
Adila Qurrota A’yun
2/20/2022
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 Lampung dengan Bengkulu 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 Lampung dengan Bengkulu Setiap Periode
plot(datainflow$Tahun,datainflow$Lampung,type = "l", col= "red")
lines(datainflow$Tahun,datainflow$Bengkulu,col="blue")
legend("top",c("Inflow Lampung","Inflow Bengkulu"),fill=c("red","blue"))
2. Komparasi Visualisasi dan Prediksi Data Outflow Uang Kartal antara Lampung dengan Bengkulu Setiap Periode
plot(dataoutflow$Tahun,dataoutflow$Lampung,type = "l", col= "green")
lines(dataoutflow$Tahun,dataoutflow$Bengkulu,col="yellow")
legend("top",c("Outflow Lampung","Outflow Bengkulu"),fill=c("green","yellow"))
3. Komparasi Visualisasi dan Prediksi Data Inflow-Outflow Uang Kartal antara Lampung dengan Bengkulu Setiap Periode
plot(datainflow$Tahun,datainflow$Lampung,type = "l", col= "red")
lines(datainflow$Tahun,datainflow$Bengkulu,col="blue")
lines(dataoutflow$Tahun,dataoutflow$Lampung,col= "green")
lines(dataoutflow$Tahun,dataoutflow$Bengkulu,col="yellow")
legend("top",c("Inflow Lampung","Inflow Bengkulu","Outflow Lampung","Outflow Bengkulu"),fill=c("red","blue","green","yellow"))
4. Komparasi Visualisasi dan Prediksi Data Inflow-Outflow Uang Kartal antara Lampung dengan Bengkulu 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$Lampung, type = "l", col = "red")
lines(datainflowperbulan$Bengkulu,col="blue")
lines(dataoutflowperbulan$Lampung, col = "green")
lines(dataoutflowperbulan$Bengkulu,col="yellow")
legend("top",c("Inflow Lampung","Inflow Bengkulu","Outflow Lampung","Outflow Bengkulu"),fill=c("red","blue","green","yellow"))
Lampungtimeseries <- datainflowperbulan$Lampung
Bengkulutimeseries <- datainflowperbulan$Bengkulu
plot.ts(Lampungtimeseries , type = "l", col = "red")
lines(Bengkulutimeseries , type = "l", col = "blue")
legend("top",c("Lampung Timeseries","Bengkulu Timeseries"),fill=c("red","blue"))
logLampung <- log(datainflowperbulan$Lampung)
logBengkulu <- log(datainflowperbulan$Bengkulu)
plot.ts(logLampung, type = "l", col = "red")
lines(logBengkulu , type = "l", col = "blue")
legend("top",c("logLampung","logBengkulu"),fill=c("red","blue"))
library(TTR)
LampungSMA3 <- SMA(datainflowperbulan$Lampung,n=3)
BengkuluSMA3 <- SMA(datainflowperbulan$Bengkulu,n=3)
plot.ts(LampungSMA3, type = "l", col = "red")
lines(BengkuluSMA3, type = "l", col = "blue")
legend("top",c("LampungSMA3","BengkuluSMA3"),fill=c("red","blue"))
library(TTR)
BengkuluSMA3 <- SMA(datainflowperbulan$Bengkulu,n=8)
LampungSMA3 <- SMA(datainflowperbulan$Lampung,n=8)
plot.ts(BengkuluSMA3, type = "l", col = "red")
lines(LampungSMA3, type = "l", col = "blue")
legend("top",c("BengkuluSMA3","LampungSMA3"),fill=c("red","blue"))
5. Komparasi Visualisasi dan Prediksi Data Inflow-Outflow Time Series Uang Kartal antara Lampung dengan Bengkulu
Lampunginflowtimeseries <- ts(datainflowperbulan$Lampung, frequency=12, start=c(2011,1))
Bengkuluinflowtimeseries <- ts(datainflowperbulan$Bengkulu, frequency=12, start=c(2011,1))
Lampunginflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 621.71179 358.56622 550.36496 340.44445 402.03710 573.97617
## 2012 1054.26685 666.52412 517.06758 282.85569 344.24522 206.42495
## 2013 234.65931 117.20955 170.13195 75.87996 74.77127 36.67275
## 2014 1433.51885 725.39736 590.72966 568.85388 487.63656 605.40178
## 2015 1360.19086 508.30661 417.04559 277.84130 383.31675 415.35766
## 2016 1390.23556 804.10302 598.27040 555.00940 286.75963 158.32177
## 2017 1134.20195 690.26841 655.03228 794.03117 675.69061 531.31408
## 2018 1802.74124 949.48387 814.34998 689.44512 370.42214 2491.07250
## 2019 2147.18917 921.91017 900.51341 1104.01903 842.23750 3364.05676
## 2020 2551.78237 1446.11620 939.33048 955.09673 1276.19200 1889.39473
## 2021 2555.46285 1243.57068 936.61307 1164.78835 2166.96750 1237.16558
## Jul Aug Sep Oct Nov Dec
## 2011 656.24294 542.87169 1775.98512 623.85717 801.97986 442.08517
## 2012 412.76796 1054.75071 949.52907 542.34897 684.54376 253.90505
## 2013 44.55553 417.59331 503.81158 545.79969 811.39285 441.34723
## 2014 405.66825 2092.45192 643.26178 797.24338 708.30613 389.36846
## 2015 1428.15031 593.52078 619.52684 913.62659 703.11334 539.77191
## 2016 2223.80337 456.30742 715.20587 756.16663 791.67218 637.28563
## 2017 2604.58586 1140.22051 1078.63653 1103.29322 928.41949 742.39402
## 2018 1695.17386 1035.94201 1075.27013 999.38822 1026.78169 465.18491
## 2019 1323.56631 1497.04385 1400.45661 1499.49687 1110.19777 935.76984
## 2020 1067.22370 1128.19519 1159.70107 738.52056 1396.10786 609.86422
## 2021 636.42925 756.16900Bengkuluinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 122.17640 42.56978 56.79831 27.06372 33.27979 25.84131
## 2012 229.63010 125.41615 65.93120 27.71178 17.46938 17.46938
## 2013 225.33676 240.39147 247.97928 232.80433 158.28819 99.59913
## 2014 708.02522 269.13089 173.04810 221.13003 102.52019 131.58252
## 2015 644.62293 221.83713 163.04665 105.55613 96.35064 84.34825
## 2016 702.39709 293.29774 185.31632 73.73894 119.25824 76.02947
## 2017 705.34454 296.38089 218.07302 108.20777 124.26259 38.37514
## 2018 885.45535 277.07756 207.05547 156.74029 120.71976 669.85657
## 2019 902.06334 384.59633 283.98631 340.23492 256.59610 1294.68991
## 2020 983.83714 517.87037 322.68228 295.68625 330.78731 594.49286
## 2021 1134.14469 507.34820 410.99660 309.79568 798.17998 293.65593
## Jul Aug Sep Oct Nov Dec
## 2011 98.70596 64.44523 430.67254 100.84602 111.67560 39.03351
## 2012 74.43659 207.95245 172.87088 104.67443 134.41372 23.27873
## 2013 135.59282 392.32979 166.69236 194.90184 165.05959 118.56169
## 2014 83.35252 899.76893 204.79900 245.78856 146.50267 75.86238
## 2015 662.75459 223.16428 168.84114 212.90720 127.31721 80.51677
## 2016 661.14587 110.45568 243.85150 175.18164 136.70141 111.48900
## 2017 919.91900 300.75244 296.76196 275.01659 201.18931 135.31315
## 2018 423.32742 286.78781 368.53402 286.96586 286.34575 181.12197
## 2019 381.33964 428.71096 432.36290 498.97557 330.91527 254.67978
## 2020 289.77418 409.26120 438.92378 281.96995 320.24937 185.53660
## 2021 350.87090 355.37500Lampungoutflowtimeseries <- ts(dataoutflowperbulan$Lampung, frequency=12, start=c(2011,1))
Bengkuluoutflowtimeseries <- ts(dataoutflowperbulan$Bengkulu, frequency=12, start=c(2011,1))
Lampungoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 171.73514 219.94503 342.64595 449.19497 435.48670 560.37199
## 2012 158.37385 143.61587 394.89727 507.72792 767.30148 655.28330
## 2013 22.45428 29.23682 110.38391 131.24521 202.68550 265.22837
## 2014 176.19089 461.64557 620.25400 823.10212 860.99213 627.46225
## 2015 79.28158 339.63124 533.63173 1128.60610 824.42210 1345.73686
## 2016 90.45391 366.34626 546.39793 569.14521 878.53762 3098.46776
## 2017 237.91153 511.86310 849.69294 966.64675 1462.29777 3500.09080
## 2018 318.72999 882.14666 1174.25565 998.17193 2665.43895 2743.98079
## 2019 404.72220 917.99585 1094.90498 1598.16522 4619.20707 177.59795
## 2020 456.44219 786.94826 1872.12587 872.29617 2180.29038 535.27930
## 2021 101.59299 535.40874 1170.44151 1897.92824 2151.89979 841.46432
## Jul Aug Sep Oct Nov Dec
## 2011 666.16768 1300.12070 85.77778 360.29523 363.14330 769.51399
## 2012 1070.05511 1224.03581 191.19995 311.82312 165.81554 785.51667
## 2013 716.58596 270.42444 682.06111 561.75263 495.94527 1083.01386
## 2014 2409.46995 269.11197 419.94532 498.62539 574.09363 598.25583
## 2015 2563.10561 767.93798 447.47874 410.26991 567.34463 938.38091
## 2016 500.52865 1026.05478 1034.08560 685.71598 788.86106 850.91234
## 2017 331.22315 1081.53768 589.27201 743.74975 1260.73121 1823.76710
## 2018 608.59506 813.89609 640.85045 760.05619 901.58457 1217.68173
## 2019 1207.01071 973.97942 907.99564 781.79512 1193.23921 1749.78046
## 2020 1538.36508 948.45298 1152.95654 1422.69234 897.20976 1210.34936
## 2021 1102.00325 249.35351Bengkuluoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 43.00021 82.23542 143.53922 246.22066 202.80478 265.84634
## 2012 77.67069 136.45409 214.08931 230.04005 343.95133 343.95133
## 2013 150.23670 309.92998 431.93072 314.02314 742.58906 664.43864
## 2014 184.84757 233.07711 359.39862 524.14915 447.54582 377.69263
## 2015 103.40197 176.91637 236.82757 435.72702 510.20743 474.21976
## 2016 59.75611 134.50325 206.17499 355.34003 506.32330 1581.42961
## 2017 156.75645 191.46206 341.51406 410.43977 612.92546 1597.77779
## 2018 104.78294 200.91583 399.37190 498.39520 866.36789 1137.64484
## 2019 136.77104 354.05007 432.66657 755.79629 1646.68269 168.74806
## 2020 256.84547 331.85653 442.42736 531.24172 969.68490 209.58637
## 2021 95.04035 340.25426 457.19172 920.71828 1096.04779 629.30605
## Jul Aug Sep Oct Nov Dec
## 2011 263.31558 497.98805 73.97831 188.67118 175.22115 377.68102
## 2012 205.01716 360.89097 153.25346 209.32113 202.05658 482.63553
## 2013 1563.65149 783.20289 262.44591 260.53121 382.27823 624.35333
## 2014 949.04614 161.37331 247.44909 317.04213 292.98312 488.31758
## 2015 1085.06420 246.35914 274.35432 250.71305 309.02593 748.71687
## 2016 212.21523 567.18382 238.44064 187.43127 384.85065 729.08792
## 2017 110.49356 216.10078 248.63583 249.51486 472.84165 838.28091
## 2018 233.48894 261.42442 225.52806 344.89425 470.11011 752.32699
## 2019 653.94175 479.32908 380.83854 386.78029 650.26438 795.78060
## 2020 680.85829 483.37874 506.16610 625.26947 575.95459 950.75046
## 2021 676.14611 466.14904plot.ts(Lampunginflowtimeseries,type = "l", col = "red")
lines(Bengkuluinflowtimeseries, type = "l", col = "blue")
legend("top",c("Lampunginflowtimeseries","Bengkuluinflowtimeseries"),fill=c("red","blue"))
plot.ts(Lampungoutflowtimeseries,type = "l", col = "green")
lines(Bengkuluoutflowtimeseries, type = "l", col = "yellow")
legend("top",c("Lampungoutflowtimeseries","Bengkuluoutflowtimeseries"),fill=c("green","yellow"))
Lampungintimeseriescomponents <- decompose(Lampunginflowtimeseries)
Bengkuluintimeseriescomponents <- decompose(Bengkuluinflowtimeseries)
Lampungintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2012 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2013 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2014 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2015 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2016 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2017 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2018 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2019 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2020 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2021 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## Jul Aug Sep Oct Nov Dec
## 2011 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2012 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2013 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2014 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2015 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2016 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2017 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2018 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2019 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2020 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2021 320.071011 118.042127Bengkuluintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2012 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2013 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2014 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2015 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2016 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2017 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2018 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2019 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2020 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2021 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## Jul Aug Sep Oct Nov Dec
## 2011 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2012 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2013 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2014 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2015 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2016 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2017 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2018 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2019 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2020 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2021 99.818044 52.992951Lampungouttimeseriescomponents <- decompose(Lampungoutflowtimeseries)
Bengkuluouttimeseriescomponents <- decompose(Bengkuluoutflowtimeseries)
Lampungouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2012 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2013 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2014 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2015 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2016 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2017 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2018 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2019 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2020 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2021 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## Jul Aug Sep Oct Nov Dec
## 2011 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2012 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2013 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2014 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2015 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2016 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2017 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2018 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2019 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2020 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2021 308.82481 14.24713Bengkuluouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2012 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2013 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2014 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2015 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2016 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2017 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2018 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2019 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2020 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2021 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## Jul Aug Sep Oct Nov Dec
## 2011 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2012 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2013 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2014 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2015 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2016 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2017 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2018 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2019 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2020 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2021 169.42047 -21.85756plot(Lampungintimeseriescomponents$seasonal,type = "l", col = "red")
lines(Bengkuluintimeseriescomponents$seasonal,col="blue")
lines(Lampungouttimeseriescomponents$seasonal, type = "l", col = "green")
lines(Bengkuluouttimeseriescomponents$seasonal,col="yellow")
legend("top",c("Lampung Inflow","Bengkulu Inflow", "Lampung Outflow","Bengkulu Outflow"),fill=c("red","blue","green","yellow"))
plot(Lampungintimeseriescomponents$trend,type = "l", col = "red")
lines(Bengkuluintimeseriescomponents$trend,col="blue")
lines(Lampungouttimeseriescomponents$trend, type = "l", col = "green")
lines(Bengkuluouttimeseriescomponents$trend,col="yellow")
legend("top",c("Lampung Inflow","Bengkulu Inflow", "Lampung Outflow","Bengkulu Outflow"),fill=c("red","blue","green","yellow"))
plot(Lampungintimeseriescomponents$random,type = "l", col = "red")
lines(Bengkuluintimeseriescomponents$random,col="blue")
lines(Lampungouttimeseriescomponents$random, type = "l", col = "green")
lines(Bengkuluouttimeseriescomponents$random,col="yellow")
legend("top",c("Lampung Inflow","Bengkulu Inflow", "Lampung Outflow","Bengkulu Outflow"),fill=c("red","blue","green","yellow"))
plot(Lampungintimeseriescomponents$figure,type = "l", col = "red")
lines(Bengkuluintimeseriescomponents$figure,col="blue")
lines(Lampungouttimeseriescomponents$figure, type = "l", col = "green")
lines(Bengkuluouttimeseriescomponents$figure,col="yellow")
legend("top",c("Lampung Inflow","Bengkulu Inflow", "Lampung Outflow","Bengkulu Outflow"),fill=c("red","blue","green","yellow"))