Visualisasi dan prediksi Data inflow dan outflow uang kartal antara jawa barat dengan jawa timur
Arvin Azaria Munsyi
16/3/2022
Dosen Pengampu : Prof. Dr. Suhartono, M.Kom
Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Jurusan : Teknik Informatika
Fakultas : Sains dan Teknologi
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2datainflowjawa <- read_excel(path = "~/linear algebra/inflow jawa.xlsx")library(readxl)
dataoutflowjawa <- read_excel(path = "~/linear algebra/outflow jawa.xlsx")datainflowjawa## # A tibble: 11 x 7
## Keterangan Jawa `Jawa Barat` `Jawa Tengah` Yogyakarta `Jawa Timur` Banten
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 123917. 43775. 35137. 6490. 38515. 0
## 2 2012 160482. 60629. 43298. 9173. 47383. 0
## 3 2013 134998. 35190. 42182. 8939. 48687. 0
## 4 2014 217303. 78660. 60476. 13890. 64276. 0
## 5 2015 230141. 81303. 65198. 14831. 68808. 0
## 6 2016 261607. 88036. 72782. 17350. 83439. 0
## 7 2017 277609. 83220. 77031. 17483. 98380. 1495.
## 8 2018 306911. 87243. 87829. 20574. 106433. 4832.
## 9 2019 324624. 94846. 90751. 20899. 113651. 4477.
## 10 2020 259444. 76883. 84970. 7348. 86848. 3396.
## 11 2021 187816. 57295. 62024. 6714. 58986. 2798.dataoutflowjawa## # A tibble: 11 x 7
## Keterangan Jawa `Jawa Barat` `Jawa Tengah` Yogyakarta `Jawa Timur` Banten
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 83511. 20782. 19975. 7538. 35217. 0
## 2 2012 111363. 28895. 28493. 9486. 44489. 0
## 3 2013 98969. 23067. 29529. 9708. 36665. 0
## 4 2014 147069. 40857. 39110. 13171. 53931. 0
## 5 2015 171568. 47063. 46840. 14080. 63585. 0
## 6 2016 190568. 49405. 53659. 13013. 74491. 0
## 7 2017 228905. 53825. 62761. 16810. 93396. 2113.
## 8 2018 253125. 61358. 69368. 20357. 97995. 4047.
## 9 2019 271957. 61692. 72363. 21353. 105514. 11035.
## 10 2020 251363. 57235. 72342. 16619. 93374. 11793.
## 11 2021 143340. 34763. 44455. 9652. 46029. 8441.Visualisasi Prediksi Data Inflow Uang Kartal antara jawa barat dengan jawa timur setiap periode
plot(datainflowjawa$Keterangan,datainflowjawa$`Jawa Barat`,type = "l", col = "red")
lines(datainflowjawa$Keterangan, datainflowjawa$`Jawa Timur`,col = "blue")
legend("top", c("inflow jawa barat","inflow jawa timur"),fill = c("red","blue"))
Visualisasi Prediksi Data Outflow Uang Kartal antara jawa barat dengan jawa timur setiap periode
plot(dataoutflowjawa$Keterangan, dataoutflowjawa$`Jawa Barat`, type = "l", col = "red")
lines(dataoutflowjawa$Keterangan, dataoutflowjawa$`Jawa Timur`, col = "blue")
legend("top", c("Outflow jawa barat","Outflow jawa timur"), fill = c("red","blue"))
Visualisasi Prediksi Data Inflow-Outflow Uang Kartal antara jawa barat dengan jawa timur setiap periode
plot(datainflowjawa$Keterangan ,datainflowjawa$`Jawa Barat` ,type = "l", col = "red")
lines(datainflowjawa$Keterangan ,datainflowjawa$`Jawa Timur` ,col = "blue")
lines(dataoutflowjawa$Keterangan,dataoutflowjawa$`Jawa Barat` ,type = "l", col = "green")
lines(dataoutflowjawa$Keterangan,dataoutflowjawa$`Jawa Timur` ,col = "orange")
legend("top",c("Inflow jawa barat","Inflow jawa timur","Outflow jawa barat","Outflow jawa timur"),fill=c("red","blue","green","orange"))
Visualisasi Prediksi Data Inflow-Outflow Uang Kartal antara jawa barat dengan jawa timur Setiap Bulan
library(readxl)
inflowjawaperbulan <- read_excel(path = "~/linear algebra/inflow jawa perbulan.xlsx")
outflowjawaperbulan <- read_excel(path = "~/linear algebra/outflow jawa perbulan.xlsx")inflowjawaperbulan## # A tibble: 128 x 7
## Keterangan Jawa `Jawa Barat` `Jawa Tengah` Yogyakarta `Jawa Timur`
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 7736. 1980. 2254. 431. 3071.
## 2 2011-02-01 00:00:00 6667. 1726. 1823. 186. 2932.
## 3 2011-03-01 00:00:00 10318. 3718. 3085. 461. 3054.
## 4 2011-04-01 00:00:00 7826. 2864. 2290. 291. 2381.
## 5 2011-05-01 00:00:00 8166. 3169. 2202. 375. 2419.
## 6 2011-06-01 00:00:00 7442. 2971. 2036. 436. 1998.
## 7 2011-07-01 00:00:00 9051. 3615. 2607. 499. 2330.
## 8 2011-08-01 00:00:00 6073. 2398. 1496. 293. 1887.
## 9 2011-09-01 00:00:00 28450. 9581. 8534. 1568. 8767.
## 10 2011-10-01 00:00:00 11368. 3975. 3340. 740. 3314.
## # ... with 118 more rows, and 1 more variable: Banten <dbl>outflowjawaperbulan## # A tibble: 128 x 7
## Keterangan Jawa `Jawa Barat` `Jawa Tengah` Yogyakarta `Jawa Timur`
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 1113. 181. 123. 186. 622.
## 2 2011-02-01 00:00:00 2304. 445. 425. 272. 1161.
## 3 2011-03-01 00:00:00 3427. 762. 535. 312. 1819.
## 4 2011-04-01 00:00:00 5427. 1246. 1165. 467. 2548.
## 5 2011-05-01 00:00:00 5168. 1110. 1372. 485. 2202.
## 6 2011-06-01 00:00:00 6644. 1417. 1717. 629. 2882.
## 7 2011-07-01 00:00:00 8652. 2034. 2318. 597. 3703.
## 8 2011-08-01 00:00:00 26309. 6858. 7200. 2185. 10065.
## 9 2011-09-01 00:00:00 2440. 541. 427. 474. 998.
## 10 2011-10-01 00:00:00 5599. 1310. 1148. 694. 2447.
## # ... with 118 more rows, and 1 more variable: Banten <dbl>plot(inflowjawaperbulan$`Jawa Barat` ,type = "l", col = "dark blue")
lines(inflowjawaperbulan$`Jawa Timur` ,col = "maroon")
lines(outflowjawaperbulan$`Jawa Barat` ,type = "l", col = "coral")
lines(outflowjawaperbulan$`Jawa Timur` ,col = "green")
legend("top",c("Inflow jawa barat","Inflow jawa timur","Outflow jawa barat","Outflow jawa timur"),fill=c("dark blue","maroon","coral","green"))
JawaBaratTimeSeries <- inflowjawaperbulan$`Jawa Barat`
JawaTimurTimeSeries <- inflowjawaperbulan$`Jawa Timur`
plot.ts(JawaBaratTimeSeries,type = "l", col = "red")
lines(JawaTimurTimeSeries, type = "l", col = "green" )
legend("top", c("jawa barat timeseries","jawa timur time series"),fill = c("red","green"))
logJawaBarat <- log(inflowjawaperbulan$`Jawa Barat`)
logJawaTimur <- log(inflowjawaperbulan$`Jawa Timur`)
plot.ts(logJawaBarat, type = "l",col = "grey")
lines(logJawaTimur, type = "l",col = "gold")
legend("top",c("logJawaBarat","logJawaTimur"),fill = c("grey","gold"))
library(TTR)## Warning: package 'TTR' was built under R version 4.1.3JawaBaratSMA3 <- SMA(inflowjawaperbulan$`Jawa Barat`,n=3)
JawaTimurSMA3 <- SMA(inflowjawaperbulan$`Jawa Timur`,n=3)
plot.ts(JawaBaratSMA3, type = "l", col = "yellow")
lines(JawaTimurSMA3, type = "l", col = "purple")
legend("top",c("JawaBaratSMA3","JawaTimurSMA3"),fill = c("yellow","purple"))
library(TTR)JawaBaratSMA3 <- SMA(inflowjawaperbulan$`Jawa Barat`,n=8)
JawaTimurSMA3 <- SMA(inflowjawaperbulan$`Jawa Timur`,n=8)
plot.ts(JawaBaratSMA3, type = "l", col = "yellow")
lines(JawaTimurSMA3, type = "l", col = "purple")
legend("top",c("JawaBaratSMA3","JawaTimurSMA3"),fill = c("yellow","purple"))
Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal antara Jawa Barat dengan Jawa Timur
jabarinflowtimeseries <- ts(inflowjawaperbulan$`Jawa Barat`, frequency = 12, start = c(2011,1))
jatiminflowtimeseries <- ts(inflowjawaperbulan$`Jawa Timur`, frequency = 12, start = c(2011,1))jabarinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 1980.3285 1725.6485 3717.8929 2863.7244 3169.4339 2971.4317
## 2012 5873.0749 4426.0191 4118.2626 4002.5015 5021.0839 5048.4072
## 2013 3194.7520 1897.3730 1082.1137 1154.3214 1294.0395 1079.9000
## 2014 8242.0860 6263.4963 5187.0695 5671.1514 4998.1296 6342.6529
## 2015 9165.0026 5296.6565 5871.9311 5619.7098 5869.8295 6577.6708
## 2016 10045.8328 6526.1852 5732.3604 5444.5314 6784.8252 5126.5606
## 2017 9127.3547 5890.5899 6514.4821 5134.7993 5744.5843 3678.3763
## 2018 10507.9216 5620.3161 6049.8859 6479.1871 5531.4095 12534.4088
## 2019 11412.1770 6589.9532 5984.6745 6411.5937 5175.4092 17164.0014
## 2020 12119.6656 6232.4385 5176.8003 5506.2201 4694.7020 9985.0523
## 2021 12965.3448 5678.6530 6112.9267 5177.0146 11698.7809 6319.1959
## Jul Aug Sep Oct Nov Dec
## 2011 3615.0001 2398.0288 9580.5553 3974.8051 4328.1571 3449.9416
## 2012 5402.5484 6685.0025 6179.0816 4405.0095 5583.0107 3884.9810
## 2013 996.9936 2424.2419 5775.2391 6370.3939 5662.7978 4257.7023
## 2014 3590.2782 13318.3519 6910.4652 7101.1196 5999.9334 5035.7368
## 2015 9821.6711 8958.7368 6227.7674 6697.1780 6106.1231 5090.4833
## 2016 14735.1467 7634.4139 7089.3179 6883.6591 5985.4179 6047.8950
## 2017 15872.1572 7310.8588 6978.3334 6589.7235 6083.1940 4295.8605
## 2018 9793.0746 6545.6836 6878.1089 7198.3731 5660.9146 4443.9625
## 2019 8678.2620 8129.2421 7007.8905 7236.5522 6532.2064 4523.6976
## 2020 5496.5412 6253.7269 6667.8679 4578.6309 7019.8521 3151.9279
## 2021 3754.6179 5588.0980jatiminflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 3071.011 2932.262 3054.203 2381.178 2418.761 1998.229 2329.895
## 2012 5551.676 4091.862 3249.951 3193.361 3851.132 2673.787 3945.991
## 2013 6189.613 3389.515 2937.854 3256.841 3189.894 2975.463 3470.218
## 2014 8821.191 5411.171 3789.147 3889.709 3960.321 4229.178 1784.529
## 2015 9302.214 4610.568 4528.315 4299.768 5006.318 4808.087 10719.480
## 2016 9802.295 7060.086 5170.786 4770.902 6282.794 4496.823 14403.892
## 2017 10336.152 6777.860 6750.998 6386.411 7526.697 3332.357 19702.729
## 2018 13187.621 7417.607 6096.684 7509.484 6441.516 15185.729 12618.539
## 2019 13282.646 7529.556 7036.191 7362.127 6503.036 19381.085 9575.539
## 2020 14488.880 8426.676 6497.389 6243.143 7031.218 9908.713 5461.708
## 2021 14673.916 7171.052 5813.241 5305.827 11546.156 6446.963 2922.981
## Aug Sep Oct Nov Dec
## 2011 1886.602 8767.383 3314.239 3747.879 2613.509
## 2012 5367.956 5415.970 3291.799 4143.024 2606.021
## 2013 7713.398 4588.796 4117.679 4097.263 2760.064
## 2014 14339.480 4988.205 5031.002 4545.631 3486.724
## 2015 6885.894 4549.856 5109.469 5009.577 3978.902
## 2016 7197.173 5917.749 6556.520 5873.714 5905.975
## 2017 8271.746 7553.481 7754.196 7510.503 6477.016
## 2018 8495.113 7821.194 8461.978 7774.122 5423.020
## 2019 8845.822 7792.708 9314.393 8651.688 8375.968
## 2020 6323.179 6604.377 5057.451 7289.093 3515.972
## 2021 5105.404jabaroutflowtimeseries <- ts(outflowjawaperbulan$`Jawa Barat`, frequency = 12, start = c(2011,1))
jatimoutflowtimeseries <- ts(outflowjawaperbulan$`Jawa Timur`, frequency = 12, start = c(2011,1))jabaroutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 181.3603 445.1327 761.6186 1246.3934 1109.7649 1416.8661
## 2012 671.6212 1002.3632 2281.1244 2467.0254 2280.4243 2765.4863
## 2013 528.7625 627.3964 1356.0477 1158.8957 1646.7584 1734.9389
## 2014 1713.3662 1914.5702 2636.1710 2143.6532 3177.4604 2777.8832
## 2015 1791.3128 2447.9196 2343.6130 4638.1146 2987.5062 4744.5294
## 2016 1286.9910 2572.6527 3143.0140 3315.2421 4207.0365 14050.5141
## 2017 1412.5942 2631.2139 4296.7833 3499.5846 4532.7897 15286.1204
## 2018 1228.3730 3600.8229 5533.7218 4389.9407 11385.8521 10835.2683
## 2019 1062.0100 3230.6399 5001.2904 5486.7615 19573.1629 866.4689
## 2020 2022.6886 2761.6234 4091.0903 5412.6772 10271.4852 1325.4509
## 2021 680.3810 2248.8929 3247.0141 10573.2876 9548.9282 2255.6891
## Jul Aug Sep Oct Nov Dec
## 2011 2034.1084 6858.3309 541.0612 1310.1216 1177.5762 3699.4895
## 2012 2598.9985 5510.8284 962.2777 1909.7261 2056.9649 4388.0579
## 2013 3272.1964 1707.8440 1713.5428 2564.9296 2360.6920 4394.6459
## 2014 10216.9924 1338.9065 2783.6008 3779.0261 3121.3091 5253.5617
## 2015 10250.2900 1832.8051 3156.7885 3433.4900 3132.0420 6304.2540
## 2016 2984.5153 1662.4221 3825.6385 2754.3698 3495.3434 6107.0954
## 2017 1319.8764 4072.1924 2251.3678 3184.0253 5454.6336 5883.4930
## 2018 2045.8298 4308.3747 2820.9496 3262.5906 4737.7032 7208.4220
## 2019 3845.6894 4560.5874 2168.6492 3908.8675 4580.5862 7407.6064
## 2020 4706.1033 3583.1857 3279.5102 7565.6162 3408.9003 8806.6301
## 2021 4237.2599 1971.0936jatimoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 622.2843 1161.2019 1818.9743 2548.3076 2202.0508 2881.6643
## 2012 972.6386 2107.9733 3537.5574 3385.7392 3330.3175 5276.1881
## 2013 1042.6989 1416.3046 1956.8638 1501.3800 2436.5230 2548.4787
## 2014 2292.6490 2337.3901 4341.3923 3259.1932 3762.0541 3671.3338
## 2015 1477.3586 2466.3076 3691.7979 6112.8089 3701.0538 7691.4778
## 2016 2028.4451 3699.0597 4496.5522 5539.6423 7636.9558 18111.9600
## 2017 2443.7613 4603.4441 7882.3392 6755.4780 8700.5238 22447.4045
## 2018 2578.4385 5969.9665 9790.7112 6163.5930 14484.9670 15809.8892
## 2019 2272.9352 5545.4638 8593.6906 9438.6568 24122.3944 2418.9280
## 2020 3789.6870 5644.7356 9175.4920 9057.1774 11911.2719 2584.3144
## 2021 1339.2973 4063.6639 5911.6155 12153.3435 10258.7903 3478.7340
## Jul Aug Sep Oct Nov Dec
## 2011 3703.3381 10065.1388 997.5488 2446.9332 2002.5094 4766.7289
## 2012 4999.0124 7700.9961 1600.9659 3363.9058 2929.6925 5283.8016
## 2013 5133.5144 3883.1844 2330.9637 4056.2800 3496.0878 6862.7322
## 2014 14441.2175 1421.8224 3508.4894 4449.9358 4017.4337 6428.3864
## 2015 13456.5557 2901.5841 5135.6666 3915.9097 4379.8290 8654.3655
## 2016 3438.6223 3697.4495 6212.4006 4866.6283 5763.2763 8999.8004
## 2017 2421.0292 8428.1973 5489.6827 5071.8714 8510.9058 10641.2201
## 2018 4579.9448 9148.7061 5728.7680 6591.1663 7346.6019 9802.3189
## 2019 7869.8703 8757.2044 6624.0955 6896.2513 8618.8669 14355.7462
## 2020 8544.1231 5712.7574 6949.3088 10935.1122 5170.0166 13900.2427
## 2021 5906.8317 2917.2180plot.ts(jabarinflowtimeseries,type = "l", col = "blue")
lines(jatiminflowtimeseries,type = "l", col = "red")
legend("top", c("Jabarinflowtimeseries","jatiminflowtimeseries"),fill = c("blue","red"))
plot.ts(jabaroutflowtimeseries,type = "l", col = "blue")
lines(jatimoutflowtimeseries,type = "l", col = "red")
legend("top", c("Jabaroutflowtimeseries","jatimoutflowtimeseries"),fill = c("blue","red"))
jabarintimeseriescomponents <- decompose(jabarinflowtimeseries)
jatimintimeseriescomponents <- decompose(jatiminflowtimeseries)jabarintimeseriescomponents## $x
## Jan Feb Mar Apr May Jun
## 2011 1980.3285 1725.6485 3717.8929 2863.7244 3169.4339 2971.4317
## 2012 5873.0749 4426.0191 4118.2626 4002.5015 5021.0839 5048.4072
## 2013 3194.7520 1897.3730 1082.1137 1154.3214 1294.0395 1079.9000
## 2014 8242.0860 6263.4963 5187.0695 5671.1514 4998.1296 6342.6529
## 2015 9165.0026 5296.6565 5871.9311 5619.7098 5869.8295 6577.6708
## 2016 10045.8328 6526.1852 5732.3604 5444.5314 6784.8252 5126.5606
## 2017 9127.3547 5890.5899 6514.4821 5134.7993 5744.5843 3678.3763
## 2018 10507.9216 5620.3161 6049.8859 6479.1871 5531.4095 12534.4088
## 2019 11412.1770 6589.9532 5984.6745 6411.5937 5175.4092 17164.0014
## 2020 12119.6656 6232.4385 5176.8003 5506.2201 4694.7020 9985.0523
## 2021 12965.3448 5678.6530 6112.9267 5177.0146 11698.7809 6319.1959
## Jul Aug Sep Oct Nov Dec
## 2011 3615.0001 2398.0288 9580.5553 3974.8051 4328.1571 3449.9416
## 2012 5402.5484 6685.0025 6179.0816 4405.0095 5583.0107 3884.9810
## 2013 996.9936 2424.2419 5775.2391 6370.3939 5662.7978 4257.7023
## 2014 3590.2782 13318.3519 6910.4652 7101.1196 5999.9334 5035.7368
## 2015 9821.6711 8958.7368 6227.7674 6697.1780 6106.1231 5090.4833
## 2016 14735.1467 7634.4139 7089.3179 6883.6591 5985.4179 6047.8950
## 2017 15872.1572 7310.8588 6978.3334 6589.7235 6083.1940 4295.8605
## 2018 9793.0746 6545.6836 6878.1089 7198.3731 5660.9146 4443.9625
## 2019 8678.2620 8129.2421 7007.8905 7236.5522 6532.2064 4523.6976
## 2020 5496.5412 6253.7269 6667.8679 4578.6309 7019.8521 3151.9279
## 2021 3754.6179 5588.0980
##
## $seasonal
## Jan Feb Mar Apr May Jun
## 2011 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## 2012 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## 2013 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## 2014 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## 2015 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## 2016 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## 2017 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## 2018 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## 2019 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## 2020 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## 2021 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## Jul Aug Sep Oct Nov Dec
## 2011 1671.0902 774.5099 711.6937 -133.8425 -386.4039 -1913.8338
## 2012 1671.0902 774.5099 711.6937 -133.8425 -386.4039 -1913.8338
## 2013 1671.0902 774.5099 711.6937 -133.8425 -386.4039 -1913.8338
## 2014 1671.0902 774.5099 711.6937 -133.8425 -386.4039 -1913.8338
## 2015 1671.0902 774.5099 711.6937 -133.8425 -386.4039 -1913.8338
## 2016 1671.0902 774.5099 711.6937 -133.8425 -386.4039 -1913.8338
## 2017 1671.0902 774.5099 711.6937 -133.8425 -386.4039 -1913.8338
## 2018 1671.0902 774.5099 711.6937 -133.8425 -386.4039 -1913.8338
## 2019 1671.0902 774.5099 711.6937 -133.8425 -386.4039 -1913.8338
## 2020 1671.0902 774.5099 711.6937 -133.8425 -386.4039 -1913.8338
## 2021 1671.0902 774.5099
##
## $trend
## Jan Feb Mar Apr May Jun Jul Aug
## 2011 NA NA NA NA NA NA 3810.110 4084.823
## 2012 4727.468 4980.573 5017.469 4893.666 4963.876 5034.289 4940.818 4723.861
## 2013 3303.280 2942.183 2747.825 2812.889 2898.104 2916.959 3142.795 3535.022
## 2014 5290.716 5852.691 6353.913 6431.661 6476.156 6522.621 6593.494 6591.664
## 2015 6956.032 7034.023 6823.926 6778.650 6766.243 6772.949 6811.931 6899.863
## 2016 7084.916 7234.464 7215.182 7258.850 7261.591 7296.453 7298.076 7233.323
## 2017 7086.212 7120.106 7102.000 7085.128 7076.955 7008.028 6992.550 7038.812
## 2018 7567.810 7282.632 7246.574 7267.758 7275.524 7264.100 7307.948 7386.027
## 2019 7725.043 7744.574 7815.964 7822.962 7860.857 7900.483 7933.284 7947.866
## 2020 7019.322 6808.604 6716.290 6591.376 6500.948 6464.109 6442.189 6454.351
## 2021 6687.458 6587.144 NA NA NA NA NA NA
## Sep Oct Nov Dec
## 2011 4214.021 4278.152 4402.753 4566.446
## 2012 4491.995 4246.815 3972.847 3652.199
## 2013 3887.984 4247.225 4589.763 4963.381
## 2014 6579.915 6606.307 6640.485 6686.598
## 2015 6945.278 6932.163 6962.989 6940.651
## 2016 7239.428 7259.111 7202.862 7099.177
## 2017 7008.192 7044.850 7091.984 7452.103
## 2018 7423.711 7418.178 7400.528 7578.594
## 2019 7899.308 7827.923 7770.169 7451.017
## 2020 6470.282 6495.570 6773.690 6912.783
## 2021
##
## $random
## Jan Feb Mar Apr May
## 2011 NA NA NA NA NA
## 2012 -1773.130504 363.735377 360.503126 390.473972 1388.547422
## 2013 -3027.265518 -126.521199 -406.002029 -376.929403 -272.725012
## 2014 32.631825 1329.094151 92.864989 521.128012 -146.686109
## 2015 -709.767064 -819.077090 307.713985 122.698336 434.926211
## 2016 42.178850 210.010054 -223.112497 -532.680367 854.574614
## 2017 -877.594956 -311.226958 672.191120 -668.690910 -1.030799
## 2018 21.374166 -744.027084 63.021042 493.067009 -412.774240
## 2019 768.395864 -236.332189 -571.580055 -129.730143 -1354.107437
## 2020 2181.605313 342.123224 -279.780811 196.482363 -474.905782
## 2021 3359.148544 9.798234 NA NA NA
## Jun Jul Aug Sep Oct
## 2011 NA -1866.200208 -2461.304414 4654.840758 -169.504317
## 2012 -1134.905987 -1209.360317 1186.631218 975.392892 292.037472
## 2013 -2986.083582 -3816.891194 -1885.290021 1175.561779 2257.011681
## 2014 -1328.992828 -4674.306160 5952.178073 -381.143424 628.654709
## 2015 -1344.302652 1338.649627 1284.364053 -1429.204112 -101.142748
## 2016 -3318.917350 5765.980896 -373.418563 -861.803679 -241.609084
## 2017 -4478.675914 7208.517127 -502.463078 -741.552768 -321.284396
## 2018 4121.284606 814.036511 -1614.853030 -1257.295882 -85.961942
## 2019 8114.494081 -926.111907 -593.133713 -1603.111312 -457.528009
## 2020 2371.918495 -2616.737854 -975.134006 -514.107732 -1783.096847
## 2021 NA NA NA
## Nov Dec
## 2011 311.807955 797.329578
## 2012 1996.567737 2146.615868
## 2013 1459.438751 1208.154569
## 2014 -254.147469 262.972534
## 2015 -470.461975 63.666168
## 2016 -831.039987 862.551331
## 2017 -622.386311 -1242.409046
## 2018 -1353.209314 -1220.797891
## 2019 -851.558981 -1013.485662
## 2020 632.566116 -1847.020928
## 2021
##
## $figure
## [1] 2918.7378 -918.2890 -1259.7090 -1281.6381 -1331.3400 1149.0246
## [7] 1671.0902 774.5099 711.6937 -133.8425 -386.4039 -1913.8338
##
## $type
## [1] "additive"
##
## attr(,"class")
## [1] "decomposed.ts"jatimintimeseriescomponents## $x
## Jan Feb Mar Apr May Jun Jul
## 2011 3071.011 2932.262 3054.203 2381.178 2418.761 1998.229 2329.895
## 2012 5551.676 4091.862 3249.951 3193.361 3851.132 2673.787 3945.991
## 2013 6189.613 3389.515 2937.854 3256.841 3189.894 2975.463 3470.218
## 2014 8821.191 5411.171 3789.147 3889.709 3960.321 4229.178 1784.529
## 2015 9302.214 4610.568 4528.315 4299.768 5006.318 4808.087 10719.480
## 2016 9802.295 7060.086 5170.786 4770.902 6282.794 4496.823 14403.892
## 2017 10336.152 6777.860 6750.998 6386.411 7526.697 3332.357 19702.729
## 2018 13187.621 7417.607 6096.684 7509.484 6441.516 15185.729 12618.539
## 2019 13282.646 7529.556 7036.191 7362.127 6503.036 19381.085 9575.539
## 2020 14488.880 8426.676 6497.389 6243.143 7031.218 9908.713 5461.708
## 2021 14673.916 7171.052 5813.241 5305.827 11546.156 6446.963 2922.981
## Aug Sep Oct Nov Dec
## 2011 1886.602 8767.383 3314.239 3747.879 2613.509
## 2012 5367.956 5415.970 3291.799 4143.024 2606.021
## 2013 7713.398 4588.796 4117.679 4097.263 2760.064
## 2014 14339.480 4988.205 5031.002 4545.631 3486.724
## 2015 6885.894 4549.856 5109.469 5009.577 3978.902
## 2016 7197.173 5917.749 6556.520 5873.714 5905.975
## 2017 8271.746 7553.481 7754.196 7510.503 6477.016
## 2018 8495.113 7821.194 8461.978 7774.122 5423.020
## 2019 8845.822 7792.708 9314.393 8651.688 8375.968
## 2020 6323.179 6604.377 5057.451 7289.093 3515.972
## 2021 5105.404
##
## $seasonal
## Jan Feb Mar Apr May Jun
## 2011 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## 2012 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## 2013 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## 2014 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## 2015 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## 2016 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## 2017 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## 2018 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## 2019 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## 2020 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## 2021 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## Jul Aug Sep Oct Nov Dec
## 2011 2040.31129 1105.68863 -56.13335 -678.91480 -665.75481 -2072.25434
## 2012 2040.31129 1105.68863 -56.13335 -678.91480 -665.75481 -2072.25434
## 2013 2040.31129 1105.68863 -56.13335 -678.91480 -665.75481 -2072.25434
## 2014 2040.31129 1105.68863 -56.13335 -678.91480 -665.75481 -2072.25434
## 2015 2040.31129 1105.68863 -56.13335 -678.91480 -665.75481 -2072.25434
## 2016 2040.31129 1105.68863 -56.13335 -678.91480 -665.75481 -2072.25434
## 2017 2040.31129 1105.68863 -56.13335 -678.91480 -665.75481 -2072.25434
## 2018 2040.31129 1105.68863 -56.13335 -678.91480 -665.75481 -2072.25434
## 2019 2040.31129 1105.68863 -56.13335 -678.91480 -665.75481 -2072.25434
## 2020 2040.31129 1105.68863 -56.13335 -678.91480 -665.75481 -2072.25434
## 2021 2040.31129 1105.68863
##
## $trend
## Jan Feb Mar Apr May Jun Jul Aug
## 2011 NA NA NA NA NA NA 3312.957 3464.635
## 2012 3839.944 4052.337 4057.752 3917.174 3932.704 3948.856 3975.125 3972.441
## 2013 3872.671 3950.574 4013.835 4013.781 4046.286 4050.798 4166.866 4360.750
## 2014 4667.107 4872.957 5165.686 5220.383 5277.120 5326.080 5376.400 5363.084
## 2015 5933.193 5994.916 5666.086 5651.091 5673.691 5713.530 5754.874 5877.774
## 2016 6306.589 6473.076 6543.042 6660.331 6756.631 6872.931 6975.470 6985.955
## 2017 7467.910 7733.468 7846.398 7964.456 8082.559 8174.552 8317.157 8462.624
## 2018 9130.518 8844.650 8865.112 8905.758 8946.233 8913.300 8873.343 8881.967
## 2019 9180.592 9068.414 9081.840 9116.170 9188.252 9347.857 9521.156 9608.796
## 2020 8591.269 8314.749 8160.125 7933.239 7699.091 7439.816 7245.027 7200.419
## 2021 6994.964 6838.443 NA NA NA NA NA NA
## Sep Oct Nov Dec
## 2011 3521.108 3563.105 3656.628 3744.458
## 2012 3930.173 3919.814 3894.907 3879.925
## 2013 4480.457 4542.297 4600.767 4685.106
## 2014 5360.524 5408.409 5469.078 5536.782
## 2015 6006.607 6053.007 6125.824 6166.041
## 2016 7040.037 7173.192 7292.334 7295.644
## 2017 8462.017 8481.549 8483.127 8931.802
## 2018 8925.778 8958.784 8955.208 9132.578
## 2019 9623.726 9554.652 9530.035 9157.360
## 2020 7119.595 7052.034 7201.101 7244.984
## 2021
##
## $random
## Jan Feb Mar Apr May
## 2011 NA NA NA NA NA
## 2012 -2244.309058 474.391149 683.809248 670.916112 1017.491630
## 2013 -1639.099577 -126.192201 415.628746 637.789506 242.671363
## 2014 198.042248 973.080379 115.070827 64.055080 -217.735987
## 2015 -587.020075 -949.481851 353.838587 43.406374 431.690289
## 2016 -460.336046 1021.876110 119.354354 -494.699275 625.226933
## 2017 -1087.798676 -520.742372 396.210519 -183.315740 543.201122
## 2018 101.061929 -992.176675 -1276.818362 -1.544325 -1405.653262
## 2019 146.012499 -1103.991055 -554.038302 -359.312784 -1586.153289
## 2020 1941.569669 546.793999 -171.126138 -295.365471 431.190679
## 2021 3722.909842 767.475271 NA NA NA
## Jun Jul Aug Sep Oct
## 2011 NA -3023.372854 -2683.721186 5302.409066 430.049197
## 2012 -2066.355069 -2069.445387 289.826687 1541.930744 50.900368
## 2013 -1866.620850 -2736.959216 2246.958942 164.472816 254.297253
## 2014 -1888.187394 -5632.182101 7870.707019 -316.186307 301.508409
## 2015 -1696.728488 2924.294358 -97.568330 -1400.617184 -264.623551
## 2016 -3167.393248 5388.111140 -894.469710 -1066.154883 62.242103
## 2017 -5633.480950 9345.261013 -1296.566687 -852.402698 -48.438313
## 2018 5481.142532 1704.884187 -1492.542641 -1048.450411 182.109039
## 2019 9241.942490 -1985.929050 -1868.662927 -1774.884400 438.656135
## 2020 1677.610456 -3823.629336 -1982.928413 -459.083988 -1315.667887
## 2021 NA NA NA
## Nov Dec
## 2011 757.006484 941.304745
## 2012 913.871754 798.349784
## 2013 162.250680 147.212093
## 2014 -257.691777 22.195974
## 2015 -450.492140 -114.885284
## 2016 -752.865679 682.584822
## 2017 -306.869889 -382.531424
## 2018 -515.331073 -1637.302865
## 2019 -212.591701 1290.862300
## 2020 753.746095 -1656.757390
## 2021
##
## $figure
## [1] 3956.04132 -434.86644 -1491.60992 -1394.72968 -1099.06353 791.28563
## [7] 2040.31129 1105.68863 -56.13335 -678.91480 -665.75481 -2072.25434
##
## $type
## [1] "additive"
##
## attr(,"class")
## [1] "decomposed.ts"jabarouttimeseriescomponents <- decompose(jabaroutflowtimeseries)
jatimouttimeseriescomponents <- decompose(jatimoutflowtimeseries)jabarouttimeseriescomponents## $x
## Jan Feb Mar Apr May Jun
## 2011 181.3603 445.1327 761.6186 1246.3934 1109.7649 1416.8661
## 2012 671.6212 1002.3632 2281.1244 2467.0254 2280.4243 2765.4863
## 2013 528.7625 627.3964 1356.0477 1158.8957 1646.7584 1734.9389
## 2014 1713.3662 1914.5702 2636.1710 2143.6532 3177.4604 2777.8832
## 2015 1791.3128 2447.9196 2343.6130 4638.1146 2987.5062 4744.5294
## 2016 1286.9910 2572.6527 3143.0140 3315.2421 4207.0365 14050.5141
## 2017 1412.5942 2631.2139 4296.7833 3499.5846 4532.7897 15286.1204
## 2018 1228.3730 3600.8229 5533.7218 4389.9407 11385.8521 10835.2683
## 2019 1062.0100 3230.6399 5001.2904 5486.7615 19573.1629 866.4689
## 2020 2022.6886 2761.6234 4091.0903 5412.6772 10271.4852 1325.4509
## 2021 680.3810 2248.8929 3247.0141 10573.2876 9548.9282 2255.6891
## Jul Aug Sep Oct Nov Dec
## 2011 2034.1084 6858.3309 541.0612 1310.1216 1177.5762 3699.4895
## 2012 2598.9985 5510.8284 962.2777 1909.7261 2056.9649 4388.0579
## 2013 3272.1964 1707.8440 1713.5428 2564.9296 2360.6920 4394.6459
## 2014 10216.9924 1338.9065 2783.6008 3779.0261 3121.3091 5253.5617
## 2015 10250.2900 1832.8051 3156.7885 3433.4900 3132.0420 6304.2540
## 2016 2984.5153 1662.4221 3825.6385 2754.3698 3495.3434 6107.0954
## 2017 1319.8764 4072.1924 2251.3678 3184.0253 5454.6336 5883.4930
## 2018 2045.8298 4308.3747 2820.9496 3262.5906 4737.7032 7208.4220
## 2019 3845.6894 4560.5874 2168.6492 3908.8675 4580.5862 7407.6064
## 2020 4706.1033 3583.1857 3279.5102 7565.6162 3408.9003 8806.6301
## 2021 4237.2599 1971.0936
##
## $seasonal
## Jan Feb Mar Apr May Jun
## 2011 -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## 2012 -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## 2013 -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## 2014 -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## 2015 -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## 2016 -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## 2017 -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## 2018 -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## 2019 -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## 2020 -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## 2021 -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## Jul Aug Sep Oct Nov Dec
## 2011 596.5215 -196.9857 -1408.0662 -440.3465 -529.0729 2025.0193
## 2012 596.5215 -196.9857 -1408.0662 -440.3465 -529.0729 2025.0193
## 2013 596.5215 -196.9857 -1408.0662 -440.3465 -529.0729 2025.0193
## 2014 596.5215 -196.9857 -1408.0662 -440.3465 -529.0729 2025.0193
## 2015 596.5215 -196.9857 -1408.0662 -440.3465 -529.0729 2025.0193
## 2016 596.5215 -196.9857 -1408.0662 -440.3465 -529.0729 2025.0193
## 2017 596.5215 -196.9857 -1408.0662 -440.3465 -529.0729 2025.0193
## 2018 596.5215 -196.9857 -1408.0662 -440.3465 -529.0729 2025.0193
## 2019 596.5215 -196.9857 -1408.0662 -440.3465 -529.0729 2025.0193
## 2020 596.5215 -196.9857 -1408.0662 -440.3465 -529.0729 2025.0193
## 2021 596.5215 -196.9857
##
## $trend
## Jan Feb Mar Apr May Jun Jul Aug
## 2011 NA NA NA NA NA NA 1752.246 1795.892
## 2012 2280.931 2248.323 2209.727 2252.262 2313.886 2379.218 2401.956 2380.380
## 2013 2068.021 1937.613 1810.458 1869.061 1909.017 1921.946 1971.579 2074.570
## 2014 2820.779 3094.774 3123.987 3219.160 3301.440 3368.920 3407.956 3433.427
## 2015 3788.587 3810.553 3846.682 3847.834 3833.884 3878.110 3900.875 3885.059
## 2016 4421.019 4111.179 4131.949 4131.521 4118.362 4125.285 4122.303 4129.977
## 2017 4304.679 4335.726 4370.539 4322.847 4422.386 4494.706 4477.714 4510.438
## 2018 4958.545 4998.634 5032.208 5059.214 5032.615 5057.949 5106.222 5083.866
## 2019 5042.011 5127.514 5110.844 5110.593 5130.974 5132.727 5181.055 5201.541
## 2020 4398.934 4394.060 4399.620 4598.271 4701.815 4711.287 4713.651 4636.357
## 2021 4972.476 4885.771 NA NA NA NA NA NA
## Sep Oct Nov Dec
## 2011 1882.422 1996.595 2096.232 2201.202
## 2012 2326.211 2233.161 2152.253 2082.911
## 2013 2181.541 2275.911 2380.722 2487.957
## 2014 3443.460 3535.206 3631.227 3705.256
## 2015 3923.565 3901.753 3897.448 4336.011
## 2016 4180.490 4236.245 4257.499 4322.556
## 2017 4602.378 4691.015 5013.657 5113.749
## 2018 5046.257 5069.773 5456.612 5382.384
## 2019 5144.073 5103.062 4712.405 4343.959
## 2020 4579.824 4759.679 4944.598 4953.252
## 2021
##
## $random
## Jan Feb Mar Apr May Jun
## 2011 NA NA NA NA NA NA
## 2012 1083.86088 372.02849 471.35288 453.13176 -2816.97890 -1732.62788
## 2013 1153.91257 307.77115 -54.45470 -471.79750 -3045.77506 -2305.90383
## 2014 1585.75791 437.78460 -87.86002 -837.13886 -2907.49617 -2709.93335
## 2015 695.89713 255.35428 -1103.11295 1028.64853 -3629.89455 -1252.47692
## 2016 -440.85730 79.46119 -588.97909 -577.91096 -2694.84227 7806.33322
## 2017 -198.91400 -86.52452 326.20030 -584.89406 -2673.11312 8672.51769
## 2018 -1037.00119 220.17654 901.46988 -430.90531 3569.71980 3658.42326
## 2019 -1286.83000 -278.88626 290.40244 614.53664 11658.67154 -6385.15474
## 2020 316.92562 -14.44823 91.42582 1052.77432 2786.15328 -5504.73290
## 2021 -1598.92434 -1018.88996 NA NA NA NA
## Jul Aug Sep Oct Nov Dec
## 2011 -314.65934 5259.42487 66.70509 -246.12665 -389.58282 -526.73170
## 2012 -399.47875 3327.43442 44.13273 116.91159 433.78501 280.12801
## 2013 704.09554 -169.74042 940.06824 729.36527 509.04328 -118.33037
## 2014 6212.51469 -1897.53471 748.20724 684.16679 19.15507 -476.71340
## 2015 5752.89313 -1855.26841 641.28998 -27.91693 -236.33265 -56.77598
## 2016 -1734.30928 -2270.56877 1053.21442 -1041.52877 -233.08267 -240.47959
## 2017 -3754.35882 -241.26009 -942.94358 -1066.64320 970.04909 -1255.27576
## 2018 -3656.91400 -578.50582 -817.24147 -1366.83639 -189.83620 -198.98096
## 2019 -1931.88703 -443.96779 -1567.35804 -753.84768 397.25420 1038.62786
## 2020 -604.06885 -856.18600 107.75267 3246.28325 -1006.62503 1828.35917
## 2021 NA NA
##
## $figure
## [1] -2693.1711 -1617.9880 -399.9559 -238.3679 2783.5169 2118.8964
## [7] 596.5215 -196.9857 -1408.0662 -440.3465 -529.0729 2025.0193
##
## $type
## [1] "additive"
##
## attr(,"class")
## [1] "decomposed.ts"jatimouttimeseriescomponents## $x
## Jan Feb Mar Apr May Jun
## 2011 622.2843 1161.2019 1818.9743 2548.3076 2202.0508 2881.6643
## 2012 972.6386 2107.9733 3537.5574 3385.7392 3330.3175 5276.1881
## 2013 1042.6989 1416.3046 1956.8638 1501.3800 2436.5230 2548.4787
## 2014 2292.6490 2337.3901 4341.3923 3259.1932 3762.0541 3671.3338
## 2015 1477.3586 2466.3076 3691.7979 6112.8089 3701.0538 7691.4778
## 2016 2028.4451 3699.0597 4496.5522 5539.6423 7636.9558 18111.9600
## 2017 2443.7613 4603.4441 7882.3392 6755.4780 8700.5238 22447.4045
## 2018 2578.4385 5969.9665 9790.7112 6163.5930 14484.9670 15809.8892
## 2019 2272.9352 5545.4638 8593.6906 9438.6568 24122.3944 2418.9280
## 2020 3789.6870 5644.7356 9175.4920 9057.1774 11911.2719 2584.3144
## 2021 1339.2973 4063.6639 5911.6155 12153.3435 10258.7903 3478.7340
## Jul Aug Sep Oct Nov Dec
## 2011 3703.3381 10065.1388 997.5488 2446.9332 2002.5094 4766.7289
## 2012 4999.0124 7700.9961 1600.9659 3363.9058 2929.6925 5283.8016
## 2013 5133.5144 3883.1844 2330.9637 4056.2800 3496.0878 6862.7322
## 2014 14441.2175 1421.8224 3508.4894 4449.9358 4017.4337 6428.3864
## 2015 13456.5557 2901.5841 5135.6666 3915.9097 4379.8290 8654.3655
## 2016 3438.6223 3697.4495 6212.4006 4866.6283 5763.2763 8999.8004
## 2017 2421.0292 8428.1973 5489.6827 5071.8714 8510.9058 10641.2201
## 2018 4579.9448 9148.7061 5728.7680 6591.1663 7346.6019 9802.3189
## 2019 7869.8703 8757.2044 6624.0955 6896.2513 8618.8669 14355.7462
## 2020 8544.1231 5712.7574 6949.3088 10935.1122 5170.0166 13900.2427
## 2021 5906.8317 2917.2180
##
## $seasonal
## Jan Feb Mar Apr May Jun
## 2011 -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## 2012 -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## 2013 -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## 2014 -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## 2015 -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## 2016 -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## 2017 -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## 2018 -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## 2019 -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## 2020 -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## 2021 -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## Jul Aug Sep Oct Nov Dec
## 2011 983.48098 281.38109 -1461.68022 -717.14347 -826.61161 2883.34315
## 2012 983.48098 281.38109 -1461.68022 -717.14347 -826.61161 2883.34315
## 2013 983.48098 281.38109 -1461.68022 -717.14347 -826.61161 2883.34315
## 2014 983.48098 281.38109 -1461.68022 -717.14347 -826.61161 2883.34315
## 2015 983.48098 281.38109 -1461.68022 -717.14347 -826.61161 2883.34315
## 2016 983.48098 281.38109 -1461.68022 -717.14347 -826.61161 2883.34315
## 2017 983.48098 281.38109 -1461.68022 -717.14347 -826.61161 2883.34315
## 2018 983.48098 281.38109 -1461.68022 -717.14347 -826.61161 2883.34315
## 2019 983.48098 281.38109 -1461.68022 -717.14347 -826.61161 2883.34315
## 2020 983.48098 281.38109 -1461.68022 -717.14347 -826.61161 2883.34315
## 2021 983.48098 281.38109
##
## $trend
## Jan Feb Mar Apr May Jun Jul Aug
## 2011 NA NA NA NA NA NA 2949.321 3003.368
## 2012 3603.371 3558.851 3485.488 3548.837 3625.677 3685.854 3710.318 3684.418
## 2013 3070.656 2917.185 2788.526 2847.792 2900.240 2989.629 3107.499 3197.959
## 2014 4173.386 4458.650 4405.157 4470.622 4508.748 4512.373 4460.304 4431.706
## 2015 4909.647 4930.276 5059.732 5105.279 5098.128 5205.977 5321.688 5396.015
## 2016 6245.630 5861.377 5939.402 6023.879 6121.136 6193.173 6224.871 6279.858
## 2017 7108.528 7263.242 7430.244 7408.682 7531.719 7714.596 7788.600 7851.150
## 2018 8036.661 8156.637 8196.620 8269.886 8284.677 8201.210 8153.527 8123.110
## 2019 8102.878 8223.646 8244.639 8294.656 8360.379 8603.116 8856.040 8923.374
## 2020 7968.487 7869.729 7756.427 7938.264 7962.847 7800.166 7679.087 7511.109
## 2021 7258.197 7031.829 NA NA NA NA NA NA
## Sep Oct Nov Dec
## 2011 3114.425 3220.925 3302.830 3449.612
## 2012 3589.736 3445.359 3329.602 3178.706
## 2013 3335.693 3508.290 3636.763 3738.779
## 2014 4410.011 4501.845 4618.204 4783.168
## 2015 5480.911 5490.560 5630.674 6228.857
## 2016 6458.616 6650.350 6745.325 6970.284
## 2017 7987.604 8042.457 8258.814 8223.269
## 2018 8055.546 8142.131 8680.152 8523.755
## 2019 8951.752 8960.099 8435.407 7933.502
## 2020 7309.237 7302.249 7362.402 7330.816
## 2021
##
## $random
## Jan Feb Mar Apr May Jun
## 2011 NA NA NA NA NA NA
## 2012 1443.33710 840.94959 84.39011 186.37696 -3099.96666 -1209.98116
## 2013 2046.11189 790.94709 -799.34193 -996.93655 -3268.32442 -3241.46506
## 2014 2193.33268 170.56777 -31.44392 -861.95428 -3551.30054 -3641.35375
## 2015 641.78124 -172.14067 -1335.61325 1357.00439 -4201.68147 -314.81431
## 2016 -143.11561 129.51007 -1410.52950 -134.76193 -1288.78747 9118.47212
## 2017 -590.69715 -367.97081 484.41583 -303.72925 -1635.80186 11932.49385
## 2018 -1384.15310 105.15710 1626.41158 -1756.81780 3395.68280 4808.36408
## 2019 -1755.87370 -386.35456 381.37238 1493.47609 12957.40866 -8984.50281
## 2020 -104.73037 66.83450 1451.38522 1468.38889 1143.81748 -8016.16644
## 2021 -1844.83016 -676.33727 NA NA NA NA
## Jul Aug Sep Oct Nov Dec
## 2011 -229.46432 6780.38932 -655.19573 -56.84870 -473.70850 -1566.22674
## 2012 305.21323 3735.19711 -527.08995 635.69040 426.70159 -778.24802
## 2013 1042.53454 403.84464 456.95126 1265.13326 685.93657 240.61011
## 2014 8997.43218 -3291.26418 560.15895 665.23438 225.84151 -1238.12493
## 2015 7151.38646 -2775.81179 1116.43588 -857.50721 -424.23378 -457.83470
## 2016 -3769.72955 -2863.79005 1215.46524 -1066.57807 -155.43708 -853.82655
## 2017 -6351.05144 295.66658 -1036.24063 -2253.44232 1078.70365 -465.39217
## 2018 -4557.06279 744.21527 -865.09800 -833.82162 -506.93832 -1604.77885
## 2019 -1969.65063 -447.55094 -865.97660 -1346.70426 1010.07115 3538.90143
## 2020 -118.44488 -2079.73316 1101.75238 4350.00695 -1365.77398 3686.08322
## 2021 NA NA
##
## $figure
## [1] -4074.06921 -2291.82747 -32.32033 -349.47499 2804.60711 2800.31496
## [7] 983.48098 281.38109 -1461.68022 -717.14347 -826.61161 2883.34315
##
## $type
## [1] "additive"
##
## attr(,"class")
## [1] "decomposed.ts"plot(jabarintimeseriescomponents$seasonal,type = "l", col = "red")
lines(jatimintimeseriescomponents$seasonal,col = "green")
lines(jabarouttimeseriescomponents$seasonal, type = "l", col = "orange")
lines(jatimouttimeseriescomponents$seasonal, col = "navy")
legend("top", c("jabar inflow","jatim inflow","jabar outflow","jatim outflow"),fill = c("red","green","orange","navy"))
plot(jabarintimeseriescomponents$trend,type = "l", col = "red")
lines(jatimintimeseriescomponents$trend,col = "green")
lines(jabarouttimeseriescomponents$trend, type = "l", col = "orange")
lines(jatimouttimeseriescomponents$trend, col = "navy")
legend("top", c("jabar inflow","jatim inflow","jabar outflow","jatim outflow"),fill = c("red","green","orange","navy"))
plot(jabarintimeseriescomponents$random,type = "l", col = "red")
lines(jatimintimeseriescomponents$random,col = "green")
lines(jabarouttimeseriescomponents$random, type = "l", col = "orange")
lines(jatimouttimeseriescomponents$random, col = "navy")
legend("top", c("jabar inflow","jatim inflow","jabar outflow","jatim outflow"),fill = c("red","green","orange","navy"))
plot(jabarintimeseriescomponents$figure,type = "l", col = "red")
lines(jatimintimeseriescomponents$figure,col = "green")
lines(jabarouttimeseriescomponents$figure, type = "l", col = "orange")
lines(jatimouttimeseriescomponents$figure, col = "navy")
legend("top", c("jabar inflow","jatim inflow","jabar outflow","jatim outflow"),fill = c("red","green","orange","navy"))