Visualisasi dan Prediksi Data Inflow-Outflow Uang kartal di Bengkulu
Mutiara Aprillia Dzakiroh
10/2/2022
Dosen Pengampu : Prof. Dr. Suhartono, M.Kom
Mata Kuliah : Linier Algebra
Prodi : Teknik Informatika
Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Pendahuluan
Bank Indonesia (BI) merupakan Bank sentral Republik Indonesia dan satu-satunya lembaga yang berwenang untuk mencetak serta mengedarkan uang rupiah kepada masyarakat. Dalam kapasitasnya sebagai bank sentral, BI mempunyai satu tujuan tunggal, yaitu mencapai dan memelihara kestabilan nilai rupiah.Adapun salah satu tugas dari BI adalah mengatur dan menjaga kelancaran sistem pembayaran. Dalam rangka mengatur dan menjaga kelancaran sistem pembayaran, BI berwenang untuk melakukan pengelolaan uang rupiah yang meliputi tahapan perencanaan, pencetakan, pengeluaran, pengedaran, pencabutan dan penarikan, serta pemusnahan uang rupiah (Bank Indonesia, 2012). Perencanaan tersebut dapat dilakukan dengan melakukan peramalan untuk inflow dan outflow uang kartal. Inflow merupakan uang yang masuk ke BI melalui kegiatan penyetoran, sedangkan outflow merupakan uang yang keluar dari BI melalui kegiatan penarikan.
Visualisasi
Berikut prediksi data Inflow-Outflow Uang Kartal di Bengkulu dan contoh penerapan visualisasi menggunakan bahasa pemrograman R.
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2inflowBI <- read_excel("~/inflowBI.xlsx")## New names:
## * `` -> ...1
## * `` -> ...3inflowBI## # A tibble: 11 x 14
## ...1 Tahun ...3 Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau
## <dbl> <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 2011 NA 57900. 2308. 23238. 9385. 3012.
## 2 2 2012 NA 65911. 2620. 25981. 11192. 4447.
## 3 3 2013 NA 98369. 36337. 18120. 14056. 8933.
## 4 4 2014 NA 86024. 4567. 30503. 14103. 6358.
## 5 5 2015 NA 86549. 4710. 30254. 13309. 7156.
## 6 6 2016 NA 97764. 5775. 34427. 14078. 8211.
## 7 7 2017 NA 103748. 5514. 35617. 15312. 8553.
## 8 8 2018 NA 117495. 5799. 41769. 15058. 10730.
## 9 9 2019 NA 133762. 7509. 47112. 14750. 10915.
## 10 10 2020 NA 109345. 6641. 36609. 10696. 9148.
## 11 11 2021 NA 89270. 3702. 31840. 10748. 7769.
## # ... with 6 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## # Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # Kep. Bangka Belitung <dbl>plot(inflowBI$Tahun,inflowBI$`Bengkulu`,type = "l", col= "dark red")
\[Visualisasi Prediksi Data Inflow Uang Kartal di- Bengkulu Setiap Periode\]
library(readxl)outflowBI <- read_excel("~/outflowBI.xlsx")## New names:
## * `` -> ...2outflowBI## # A tibble: 11 x 13
## Tahun ...2 Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau
## <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 NA 80092. 6338. 22176. 5300. 12434.
## 2 2012 NA 85235. 6378. 22495. 6434. 13014.
## 3 2013 NA 103288. 23278. 19235. 6511. 15460.
## 4 2014 NA 102338. 8630. 26391. 7060. 15158.
## 5 2015 NA 109186. 9637. 27877. 7471. 15789.
## 6 2016 NA 121992. 11311. 31959. 9198. 17645.
## 7 2017 NA 133606. 11760. 35243. 10754. 18128.
## 8 2018 NA 135676. 11450. 36908. 8447. 17926.
## 9 2019 NA 153484. 13087. 44051. 9465. 19277.
## 10 2020 NA 140589. 12874. 39758. 8763. 19139.
## 11 2021 NA 86627. 5770. 23453. 5941. 12631.
## # ... with 6 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## # Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # Kep. Bangka Belitung <dbl>plot(outflowBI$Tahun,outflowBI$`Bengkulu`,type = "l", col= "salmon")
\[Visualisasi Prediksi Data Outflow Uang Kartal di -Bengkulu Setiap Periode\]
plot(inflowBI$Tahun,inflowBI$`Bengkulu`,type = "l", col= "dark red")
lines(outflowBI$Tahun,outflowBI$`Bengkulu`,col="salmon")
legend("top",c("inflow","outflow"),fill=c("dark red","salmon"))
\[Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di -Bengkulu Setiap Periode\]
Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Bengkulu Setiap Bulan
library(readxl)
inflowPerBulan <- read_excel("~/inflowPerBulan.xlsx")## New names:
## * `` -> ...2inflowPerBulan## # 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>library(readxl)
outflowPerBulan <- read_excel("~/outflowPerBulan.xlsx")## New names:
## * `` -> ...2outflowPerBulan## # 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(inflowPerBulan$`Bengkulu`, type = "l", col = "gold")
lines(outflowPerBulan$`Bengkulu`,col="coral")
legend("top",c("Inflow","Outflow"),fill=c("gold","coral"))
Bengkulutimeseries <- inflowPerBulan$`Bengkulu`
plot.ts(Bengkulutimeseries , type = "l", col = "sky blue")
logBengkulu <- log(inflowPerBulan$`Bengkulu`)
plot.ts(logBengkulu, type = "l", col = "dark green")
library(TTR)## Warning: package 'TTR' was built under R version 4.1.2BengkuluSMA3 <- SMA(inflowPerBulan$`Bengkulu`,n=5)
plot.ts(BengkuluSMA3 )
library(TTR)
BengkuluSMA3 <- SMA(inflowPerBulan$`Bengkulu`,n=12)
plot.ts(BengkuluSMA3 )
Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Bengkulu
Bengkuluinflowtimeseries <- ts(inflowPerBulan$`Bengkulu`, frequency=12, start=c(2011,1))
Bengkuluinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 122.17640 42.56978 56.79831 27.06372 33.27979 25.84131
## 2012 229.63010 125.41615 65.93120 27.71178 17.46938 17.46938
## 2013 225.33676 240.39147 247.97928 232.80433 158.28819 99.59913
## 2014 708.02522 269.13089 173.04810 221.13003 102.52019 131.58252
## 2015 644.62293 221.83713 163.04665 105.55613 96.35064 84.34825
## 2016 702.39709 293.29774 185.31632 73.73894 119.25824 76.02947
## 2017 705.34454 296.38089 218.07302 108.20777 124.26259 38.37514
## 2018 885.45535 277.07756 207.05547 156.74029 120.71976 669.85657
## 2019 902.06334 384.59633 283.98631 340.23492 256.59610 1294.68991
## 2020 983.83714 517.87037 322.68228 295.68625 330.78731 594.49286
## 2021 1134.14469 507.34820 410.99660 309.79568 798.17998 293.65593
## Jul Aug Sep Oct Nov Dec
## 2011 98.70596 64.44523 430.67254 100.84602 111.67560 39.03351
## 2012 74.43659 207.95245 172.87088 104.67443 134.41372 23.27873
## 2013 135.59282 392.32979 166.69236 194.90184 165.05959 118.56169
## 2014 83.35252 899.76893 204.79900 245.78856 146.50267 75.86238
## 2015 662.75459 223.16428 168.84114 212.90720 127.31721 80.51677
## 2016 661.14587 110.45568 243.85150 175.18164 136.70141 111.48900
## 2017 919.91900 300.75244 296.76196 275.01659 201.18931 135.31315
## 2018 423.32742 286.78781 368.53402 286.96586 286.34575 181.12197
## 2019 381.33964 428.71096 432.36290 498.97557 330.91527 254.67978
## 2020 289.77418 409.26120 438.92378 281.96995 320.24937 185.53660
## 2021 350.87090 355.37500plot.ts(Bengkuluinflowtimeseries, type = "l", col = "sky blue")
Bengkuluoutflowtimeseries <- ts(outflowPerBulan$`Bengkulu`, frequency=12, start=c(2011,1))
Bengkuluoutflowtimeseries## 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(Bengkuluoutflowtimeseries, type = "l", col = "dark green")
Bengkuluintimeseriescomponents <- decompose(Bengkuluinflowtimeseries)
Bengkuluintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2012 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2013 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2014 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2015 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2016 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2017 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2018 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2019 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2020 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2021 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## Jul Aug Sep Oct Nov Dec
## 2011 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2012 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2013 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2014 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2015 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2016 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2017 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2018 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2019 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2020 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2021 99.818044 52.992951Bengkuluouttimeseriescomponents <- decompose(Bengkuluoutflowtimeseries)
Bengkuluouttimeseriescomponents$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(Bengkuluintimeseriescomponents$seasonal,type = "l", col = "sky blue")
lines(Bengkuluouttimeseriescomponents$seasonal,col="dark green")
legend("top",c("Inflow","Outflow"),fill=c("sky blue","dark green"))
plot(Bengkuluintimeseriescomponents$trend,type = "l", col = "sky blue")
lines(Bengkuluouttimeseriescomponents$trend,col="dark green")
legend("top",c("Inflow","Outflow"),fill=c("sky blue","dark green"))
plot(Bengkuluintimeseriescomponents$random,type = "l", col = "sky blue")
lines(Bengkuluouttimeseriescomponents$random,col="dark green")
legend("top",c("Inflow","Outflow"),fill=c("sky blue","dark green"))
plot(Bengkuluintimeseriescomponents$figure,type = "l", col = "sky blue")
lines(Bengkuluouttimeseriescomponents$figure,col="dark green")
legend("top",c("Inflow","Outflow"),fill=c("sky blue","dark green"))
Bengkuluintimeseriesseasonallyadjusted <- Bengkulutimeseries - Bengkuluintimeseriescomponents$seasonal
plot(Bengkuluintimeseriesseasonallyadjusted)
Bengkuluseriesforecasts <- HoltWinters(Bengkulutimeseries, beta=FALSE, gamma=FALSE)
Bengkuluseriesforecasts## Holt-Winters exponential smoothing without trend and without seasonal component.
##
## Call:
## HoltWinters(x = Bengkulutimeseries, beta = FALSE, gamma = FALSE)
##
## Smoothing parameters:
## alpha: 0.07206601
## beta : FALSE
## gamma: FALSE
##
## Coefficients:
## [,1]
## a 436.0149Bengkuluseriesforecasts$fitted## Time Series:
## Start = 2
## End = 128
## Frequency = 1
## xhat level
## 2 122.17640 122.17640
## 3 116.43947 116.43947
## 4 112.14137 112.14137
## 5 106.01016 106.01016
## 6 100.76877 100.76877
## 7 95.36905 95.36905
## 8 95.60953 95.60953
## 9 93.36364 93.36364
## 10 117.67215 117.67215
## 11 116.45956 116.45956
## 12 116.11480 116.11480
## 13 110.55986 110.55986
## 14 119.14077 119.14077
## 15 119.59301 119.59301
## 16 115.72582 115.72582
## 17 109.38300 109.38300
## 18 102.75915 102.75915
## 19 96.61266 96.61266
## 20 95.01452 95.01452
## 21 103.15350 103.15350
## 22 108.17776 108.17776
## 23 107.92529 107.92529
## 24 109.83420 109.83420
## 25 103.59649 103.59649
## 26 112.36983 112.36983
## 27 121.59584 121.59584
## 28 130.70379 130.70379
## 29 138.06177 138.06177
## 30 139.51941 139.51941
## 31 136.64251 136.64251
## 32 136.56686 136.56686
## 33 154.99868 154.99868
## 34 155.84140 155.84140
## 35 158.65633 158.65633
## 36 159.11778 159.11778
## 37 156.19507 156.19507
## 38 195.96327 195.96327
## 39 201.23617 201.23617
## 40 199.20477 199.20477
## 41 200.78483 200.78483
## 42 193.70329 193.70329
## 43 189.22649 189.22649
## 44 181.59658 181.59658
## 45 233.35240 233.35240
## 46 231.29467 231.29467
## 47 232.33919 232.33919
## 48 226.15329 226.15329
## 49 215.32242 215.32242
## 50 246.26040 246.26040
## 51 244.50031 244.50031
## 52 238.63027 238.63027
## 53 229.04015 229.04015
## 54 219.47774 219.47774
## 55 209.73950 209.73950
## 56 242.38649 242.38649
## 57 241.00122 241.00122
## 58 235.80093 235.80093
## 59 234.15107 234.15107
## 60 226.45198 226.45198
## 61 215.93501 215.93501
## 62 250.99240 250.99240
## 63 254.04117 254.04117
## 64 249.08845 249.08845
## 65 236.45171 236.45171
## 66 228.00604 228.00604
## 67 217.05370 217.05370
## 68 249.05765 249.05765
## 69 239.06916 239.06916
## 70 239.41380 239.41380
## 71 234.78485 234.78485
## 72 227.71636 227.71636
## 73 219.34032 219.34032
## 74 254.36471 254.36471
## 75 257.39265 257.39265
## 76 254.55904 254.55904
## 77 244.01208 244.01208
## 78 235.38222 235.38222
## 79 221.18470 221.18470
## 80 271.53970 271.53970
## 81 273.64494 273.64494
## 82 275.31089 275.31089
## 83 275.28968 275.28968
## 84 269.94957 269.94957
## 85 260.24686 260.24686
## 86 305.30314 305.30314
## 87 303.26903 303.26903
## 88 296.33531 296.33531
## 89 286.27525 286.27525
## 90 274.34433 274.34433
## 91 302.84732 302.84732
## 92 311.52984 311.52984
## 93 309.74678 309.74678
## 94 313.98334 313.98334
## 95 312.03630 312.03630
## 96 310.18488 310.18488
## 97 300.88383 300.88383
## 98 344.20844 344.20844
## 99 347.11904 347.11904
## 100 342.56931 342.56931
## 101 342.40108 342.40108
## 102 336.21746 336.21746
## 103 405.29075 405.29075
## 104 403.56469 403.56469
## 105 405.37688 405.37688
## 106 407.32165 407.32165
## 107 413.92679 413.92679
## 108 407.94448 407.94448
## 109 396.89930 396.89930
## 110 439.19757 439.19757
## 111 444.86721 444.86721
## 112 436.06183 436.06183
## 113 425.94552 425.94552
## 114 419.08785 419.08785
## 115 431.72859 431.72859
## 116 421.49850 421.49850
## 117 420.61660 420.61660
## 118 421.93593 421.93593
## 119 411.84914 411.84914
## 120 405.24791 405.24791
## 121 389.41419 389.41419
## 122 443.08395 443.08395
## 123 447.71522 447.71522
## 124 445.06905 445.06905
## 125 435.32044 435.32044
## 126 461.47028 461.47028
## 127 449.37657 449.37657
## 128 442.27766 442.27766plot(Bengkuluseriesforecasts)
library(forecast)## Warning: package 'forecast' was built under R version 4.1.2## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoodftimeseries.hw <- HoltWinters(Bengkulutimeseries, gamma=FALSE)Bengkulutimeserieforecasts2 <-forecast(dftimeseries.hw,h=20)
Bengkulutimeserieforecasts2## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 129 444.6501 129.234885 760.0654 -37.73587 927.0362
## 130 445.4646 125.338420 765.5908 -44.12615 935.0554
## 131 446.2791 119.293907 773.2642 -53.80158 946.3597
## 132 447.0935 110.811212 783.3759 -67.20590 961.3930
## 133 447.9080 99.678566 796.1374 -84.66296 980.4790
## 134 448.7225 85.767151 811.6778 -106.36979 1003.8147
## 135 449.5369 69.027118 830.0467 -132.40261 1031.4765
## 136 450.3514 49.476525 851.2263 -162.73381 1063.4366
## 137 451.1659 27.186092 875.1456 -197.25524 1099.5870
## 138 451.9803 2.262952 901.6977 -235.80304 1139.7637
## 139 452.7948 -25.164129 930.7537 -278.18029 1183.7699
## 140 453.6092 -54.956702 962.1752 -324.17525 1231.3937
## 141 454.4237 -86.975375 995.8228 -373.57474 1282.4222
## 142 455.2382 -121.085615 1031.5620 -426.17300 1336.6493
## 143 456.0526 -157.161187 1069.2665 -481.77698 1393.8823
## 144 456.8671 -195.085870 1108.8201 -540.20893 1453.9431
## 145 457.6816 -234.753996 1150.1171 -601.30724 1516.6704
## 146 458.4960 -276.070262 1193.0623 -664.92617 1581.9182
## 147 459.3105 -318.949113 1237.5701 -730.93486 1649.5559
## 148 460.1250 -363.313927 1283.5638 -799.21614 1719.4661plot(Bengkulutimeserieforecasts2)
Bengkulutimeseriesdiff1<-diff(Bengkulutimeseries, difference=1)
plot.ts(Bengkulutimeseriesdiff1)
acf(Bengkulutimeseriesdiff1, lag.max=20) # plot correlogram
acf(Bengkulutimeseriesdiff1,lag.max=20, plot=FALSE) #to get the partial autocorrelation values##
## Autocorrelations of series 'Bengkulutimeseriesdiff1', by lag
##
## 0 1 2 3 4 5 6 7 8 9 10
## 1.000 -0.499 0.013 -0.071 -0.009 0.030 0.074 0.061 -0.077 -0.012 -0.106
## 11 12 13 14 15 16 17 18 19 20
## -0.151 0.585 -0.339 -0.007 -0.024 -0.058 0.054 0.104 0.027 -0.099pacf(Bengkulutimeseriesdiff1,lag.max=20) #plot partial correlogram