Visualisasi dan Prediksi Data Inflow-Outflow Uang kartal di Aceh
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 Aceh 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$Tahun,inflowBI$`Aceh`,type = "l", col= "dark red")
\[Visualisasi Prediksi Data Inflow Uang Kartal di- Aceh Setiap Periode\]
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$Tahun,outflowBI$`Aceh`,type = "l", col= "salmon")
\[Visualisasi Prediksi Data Outflow Uang Kartal di -Aceh Setiap Periode\]
plot(inflowBI$Tahun,inflowBI$`Aceh`,type = "l", col= "dark red")
lines(outflowBI$Tahun,outflowBI$`Aceh`,col="salmon")
legend("top",c("inflow","outflow"),fill=c("dark red","salmon"))
\[Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di -Aceh Setiap Periode\]
Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Aceh 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$`Aceh`, type = "l", col = "gold")
lines(outflowPerBulan$`Aceh`,col="coral")
legend("top",c("Inflow","Outflow"),fill=c("gold","coral"))
Acehtimeseries <- inflowPerBulan$`Aceh`
plot.ts(Acehtimeseries , type = "l", col = "sky blue")
logAceh <- log(inflowPerBulan$`Aceh`)
plot.ts(logAceh, type = "l", col = "dark green")
library(TTR)## Warning: package 'TTR' was built under R version 4.1.2AcehSMA3 <- SMA(inflowPerBulan$`Aceh`,n=5)
plot.ts(AcehSMA3 )
Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Aceh
Acehinflowtimeseries <- ts(inflowPerBulan$`Aceh`, frequency=12, start=c(2011,1))
Acehinflowtimeseries## 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 424.17610plot.ts(Acehinflowtimeseries, type = "l", col = "sky blue")
Acehoutflowtimeseries <- ts(outflowPerBulan$`Aceh`, frequency=12, start=c(2011,1))
Acehoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 349.57673 192.62487 230.35748 528.59007 523.47023 405.84701
## 2012 420.97459 217.70857 503.88607 600.25342 429.26734 606.25526
## 2013 758.75245 1850.82994 2442.65886 1618.83372 2777.13063 2209.74012
## 2014 288.10448 489.92887 504.83732 773.93194 485.84142 912.38357
## 2015 269.29171 255.71308 521.69449 1125.85624 564.44034 1011.07770
## 2016 307.32375 172.45727 730.75026 667.74179 1079.70825 2642.66616
## 2017 247.27680 344.01370 677.88709 850.88747 1157.54011 2346.78323
## 2018 120.03917 266.02669 996.17473 707.23188 1634.43230 1889.68997
## 2019 85.36298 400.92165 964.22663 1218.54729 3312.30047 122.91218
## 2020 182.38950 426.10026 1433.83382 1432.33902 1689.68700 436.00470
## 2021 56.98918 60.56520 591.41875 1789.00566 2112.99646 176.09492
## Jul Aug Sep Oct Nov Dec
## 2011 957.58488 1046.09243 123.98156 634.45751 595.14381 750.32697
## 2012 600.85083 791.04331 303.81795 854.83456 207.40890 841.70726
## 2013 5383.25551 2570.21842 566.69494 895.80335 699.91951 1504.22825
## 2014 1538.16507 285.80192 611.06198 902.31059 586.60778 1250.89508
## 2015 1558.53777 301.57675 1040.79937 316.39942 824.14892 1847.02405
## 2016 692.69059 653.34617 1027.41562 515.70143 962.49370 1858.31057
## 2017 282.09425 1520.49864 354.31840 667.42332 766.85433 2544.67010
## 2018 278.98765 1155.80705 609.61619 549.33587 622.53291 2619.93560
## 2019 687.35821 1230.78590 600.44006 552.87679 1055.67352 2855.56524
## 2020 1768.67777 455.94178 829.58521 1174.85940 774.36491 2269.89617
## 2021 662.04381 320.71844plot.ts(Acehoutflowtimeseries, type = "l", col = "dark green")
Acehintimeseriescomponents <- decompose(Acehinflowtimeseries)
Acehintimeseriescomponents$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 624.31306Acehouttimeseriescomponents <- decompose(Acehoutflowtimeseries)
Acehouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2012 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2013 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2014 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2015 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2016 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2017 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2018 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2019 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2020 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2021 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## Jul Aug Sep Oct Nov Dec
## 2011 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2012 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2013 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2014 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2015 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2016 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2017 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2018 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2019 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2020 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2021 414.184121 42.244469plot(Acehintimeseriescomponents$seasonal,type = "l", col = "sky blue")
lines(Acehouttimeseriescomponents$seasonal,col="dark green")
legend("top",c("Inflow","Outflow"),fill=c("sky blue","dark green"))
plot(Acehintimeseriescomponents$trend,type = "l", col = "sky blue")
lines(Acehouttimeseriescomponents$trend,col="dark green")
legend("top",c("Inflow","Outflow"),fill=c("sky blue","dark green"))
plot(Acehintimeseriescomponents$random,type = "l", col = "sky blue")
lines(Acehouttimeseriescomponents$random,col="dark green")
legend("top",c("Inflow","Outflow"),fill=c("sky blue","dark green"))
plot(Acehintimeseriescomponents$figure,type = "l", col = "sky blue")
lines(Acehouttimeseriescomponents$figure,col="dark green")
legend("top",c("Inflow","Outflow"),fill=c("sky blue","dark green"))
Acehintimeseriesseasonallyadjusted <- Acehtimeseries - Acehintimeseriescomponents$seasonal
plot(Acehintimeseriesseasonallyadjusted)
Acehseriesforecasts <- HoltWinters(Acehtimeseries, beta=FALSE, gamma=FALSE)
Acehseriesforecasts## Holt-Winters exponential smoothing without trend and without seasonal component.
##
## Call:
## HoltWinters(x = Acehtimeseries, beta = FALSE, gamma = FALSE)
##
## Smoothing parameters:
## alpha: 0.4152191
## beta : FALSE
## gamma: FALSE
##
## Coefficients:
## [,1]
## a 407.8737Acehseriesforecasts$fitted## Time Series:
## Start = 2
## End = 128
## Frequency = 1
## xhat level
## 2 124.3333 124.3333
## 3 120.5174 120.5174
## 4 134.5928 134.5928
## 5 129.4402 129.4402
## 6 126.6634 126.6634
## 7 136.9242 136.9242
## 8 124.5923 124.5923
## 9 149.1952 149.1952
## 10 338.7129 338.7129
## 11 263.5306 263.5306
## 12 273.3779 273.3779
## 13 233.0258 233.0258
## 14 267.3344 267.3344
## 15 277.8160 277.8160
## 16 233.1030 233.1030
## 17 194.1682 194.1682
## 18 183.1225 183.1225
## 19 156.6345 156.6345
## 20 173.2366 173.2366
## 21 275.7241 275.7241
## 22 280.1212 280.1212
## 23 223.1443 223.1443
## 24 250.3179 250.3179
## 25 179.8023 179.8023
## 26 2418.3957 2418.3957
## 27 2849.7596 2849.7596
## 28 3017.4308 3017.4308
## 29 3332.0253 3332.0253
## 30 3487.0494 3487.0494
## 31 3471.5727 3471.5727
## 32 3465.2402 3465.2402
## 33 5562.4833 5562.4833
## 34 3354.1140 3354.1140
## 35 2118.9615 2118.9615
## 36 1373.2433 1373.2433
## 37 888.3608 888.3608
## 38 842.9546 842.9546
## 39 630.8108 630.8108
## 40 472.2339 472.2339
## 41 384.4515 384.4515
## 42 294.6506 294.6506
## 43 253.2647 253.2647
## 44 220.3496 220.3496
## 45 671.1817 671.1817
## 46 505.2074 505.2074
## 47 484.1343 484.1343
## 48 374.0919 374.0919
## 49 284.1737 284.1737
## 50 513.5413 513.5413
## 51 456.6188 456.6188
## 52 398.8684 398.8684
## 53 342.4817 342.4817
## 54 306.8411 306.8411
## 55 344.9392 344.9392
## 56 607.7749 607.7749
## 57 561.1832 561.1832
## 58 402.5921 402.5921
## 59 342.4129 342.4129
## 60 294.5757 294.5757
## 61 223.4793 223.4793
## 62 497.3715 497.3715
## 63 497.8042 497.8042
## 64 391.7770 391.7770
## 65 320.0304 320.0304
## 66 311.0757 311.0757
## 67 368.8927 368.8927
## 68 786.4293 786.4293
## 69 588.9184 588.9184
## 70 568.1891 568.1891
## 71 511.7722 511.7722
## 72 424.5096 424.5096
## 73 341.6868 341.6868
## 74 665.0133 665.0133
## 75 576.9138 576.9138
## 76 481.5821 481.5821
## 77 381.5710 381.5710
## 78 347.5374 347.5374
## 79 284.1364 284.1364
## 80 643.5583 643.5583
## 81 485.9664 485.9664
## 82 544.8179 544.8179
## 83 470.3047 470.3047
## 84 389.4961 389.4961
## 85 300.8454 300.8454
## 86 707.1428 707.1428
## 87 565.7311 565.7311
## 88 446.6192 446.6192
## 89 370.3565 370.3565
## 90 336.3651 336.3651
## 91 614.0304 614.0304
## 92 684.8716 684.8716
## 93 553.8133 553.8133
## 94 500.7613 500.7613
## 95 435.7051 435.7051
## 96 355.7676 355.7676
## 97 270.5780 270.5780
## 98 695.4739 695.4739
## 99 641.6604 641.6604
## 100 540.1864 540.1864
## 101 458.2456 458.2456
## 102 442.5491 442.5491
## 103 904.4292 904.4292
## 104 725.4117 725.4117
## 105 708.5559 708.5559
## 106 582.7280 582.7280
## 107 534.7594 534.7594
## 108 493.8495 493.8495
## 109 482.5089 482.5089
## 110 963.9331 963.9331
## 111 851.3327 851.3327
## 112 621.1916 621.1916
## 113 480.1125 480.1125
## 114 483.8912 483.8912
## 115 737.6820 737.6820
## 116 538.4334 538.4334
## 117 561.0343 561.0343
## 118 498.4853 498.4853
## 119 405.1111 405.1111
## 120 418.8086 418.8086
## 121 315.7178 315.7178
## 122 501.3488 501.3488
## 123 495.7709 495.7709
## 124 443.0998 443.0998
## 125 387.1425 387.1425
## 126 461.6322 461.6322
## 127 478.6469 478.6469
## 128 396.2983 396.2983plot(Acehseriesforecasts)
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(Acehtimeseries, gamma=FALSE)Acehtimeserieforecasts2 <-forecast(dftimeseries.hw,h=20)
Acehtimeserieforecasts2## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 129 385.7352 -831.9371 1603.407 -1476.534 2248.004
## 130 376.5451 -942.5304 1695.621 -1640.807 2393.897
## 131 367.3550 -1045.8663 1780.576 -1793.981 2528.691
## 132 358.1649 -1143.3107 1859.641 -1938.144 2654.474
## 133 348.9749 -1235.8480 1933.798 -2074.802 2772.752
## 134 339.7848 -1324.2158 2003.785 -2205.084 2884.654
## 135 330.5947 -1408.9835 2070.173 -2329.861 2991.050
## 136 321.4046 -1490.6016 2133.411 -2449.820 3092.629
## 137 312.2145 -1569.4339 2193.863 -2565.518 3189.948
## 138 303.0244 -1645.7791 2251.828 -2677.413 3283.462
## 139 293.8344 -1719.8860 2307.555 -2785.885 3373.554
## 140 284.6443 -1791.9645 2361.253 -2891.255 3460.543
## 141 275.4542 -1862.1937 2413.102 -2993.796 3544.705
## 142 266.2641 -1930.7277 2463.256 -3093.745 3626.273
## 143 257.0740 -1997.7003 2511.848 -3191.306 3705.454
## 144 247.8839 -2063.2287 2558.997 -3286.658 3782.426
## 145 238.6939 -2127.4161 2604.804 -3379.959 3857.347
## 146 229.5038 -2190.3538 2649.361 -3471.349 3930.356
## 147 220.3137 -2252.1233 2692.751 -3560.952 4001.580
## 148 211.1236 -2312.7978 2735.045 -3648.881 4071.128plot(Acehtimeserieforecasts2)
Acehtimeseriesdiff1<-diff(Acehtimeseries, difference=1)
plot.ts(Acehtimeseriesdiff1)
acf(Acehtimeseriesdiff1, lag.max=20) # plot correlogram
acf(Acehtimeseriesdiff1,lag.max=20, plot=FALSE) #to get the partial autocorrelation values##
## Autocorrelations of series 'Acehtimeseriesdiff1', by lag
##
## 0 1 2 3 4 5 6 7 8 9 10
## 1.000 -0.436 -0.008 0.018 -0.029 0.025 -0.059 0.302 -0.313 0.014 -0.033
## 11 12 13 14 15 16 17 18 19 20
## -0.096 0.239 -0.133 0.016 -0.022 -0.033 0.054 -0.006 0.057 -0.062pacf(Acehtimeseriesdiff1,lag.max=20) #plot partial correlogram