carga de datos
library(readxl)
library(forecast)
library(magrittr)
SERIEIVAE <- read_excel("~/EMA1182022/IVAE/IVAE_SLV.xlsx",col_types = c("skip", "numeric"), skip = 5)
SERIEIVAE.ts <- ts(data = SERIEIVAE, start = c(2009, 1), frequency = 12)
SERIEIVAE.ts %>% autoplot(main = "IVAE, El Salvador 2009-2021[marzo]", xlab = "Años/Meses", ylab = "Indice")
Yt <- SERIEIVAE.ts
Estimación del modelo de Holt Winters Estacional Clase
library(forecast)
Modelo_HoltWinters <-
HoltWinters(Yt, seasonal = "multiplicative", optim.start = c(.9, .9, .9))
Modelo_HoltWinters
## Holt-Winters exponential smoothing with trend and multiplicative seasonal component.
##
## Call:
## HoltWinters(x = Yt, seasonal = "multiplicative", optim.start = c(0.9, 0.9, 0.9))
##
## Smoothing parameters:
## alpha: 0.8408163
## beta : 0
## gamma: 1
##
## Coefficients:
## [,1]
## a 117.0799442
## b 0.1600306
## s1 0.9502255
## s2 1.0233274
## s3 1.0518154
## s4 0.9900276
## s5 1.0007537
## s6 0.9807088
## s7 0.9600206
## s8 1.0149628
## s9 1.0915423
## s10 0.9796752
## s11 0.9584676
## s12 0.9994880
Generación del pronostico
Pronostico<-forecast(Modelo_HoltWinters,h=12,level=c(.95))
Pronostico
## Point Forecast Lo 95 Hi 95
## Apr 2021 111.4044 105.94952 116.8593
## May 2021 120.1386 112.77972 127.4976
## Jun 2021 123.6515 114.83356 132.4694
## Jul 2021 116.5461 107.01096 126.0813
## Aug 2021 117.9689 107.30373 128.6341
## Sep 2021 115.7630 104.33417 127.1918
## Oct 2021 113.4746 101.36814 125.5810
## Nov 2021 120.1312 106.57272 133.6897
## Dec 2021 129.3698 114.12877 144.6109
## Jan 2022 116.2681 101.78191 130.7543
## Feb 2022 113.9046 98.99575 128.8134
## Mar 2022 118.9394 81.61739 156.2614
Pronostico %>% autoplot(main = "Pronostico IVAE, El Salvador 2009-2022[marzo]", xlab = "Años/Meses", ylab = "Indice")
Aproximación por Espacios de los Estados
Pronostico2<-hw(Yt, h = 12, level = c(.95), seasonal = "multiplicative", initial = "optimal")
Pronostico2
## Point Forecast Lo 95 Hi 95
## Apr 2021 107.4928 99.59377 115.3918
## May 2021 115.3370 106.85846 123.8155
## Jun 2021 115.6946 107.18632 124.2029
## Jul 2021 109.6301 101.56399 117.6962
## Aug 2021 112.2047 103.94477 120.4646
## Sep 2021 110.2284 102.10923 118.3476
## Oct 2021 107.9393 99.98348 115.8951
## Nov 2021 114.4306 105.99022 122.8710
## Dec 2021 122.6459 113.59234 131.6994
## Jan 2022 108.5830 100.56060 116.6054
## Feb 2022 107.1239 99.20182 115.0460
## Mar 2022 113.1678 104.79016 121.5455
Pronostico2 %>% autoplot(main = "Pronostico IVAE, El Salvador 2009-2022[marzo]", xlab = "Años/Meses", ylab = "Indice")
Estimación del modelo de Holt Winters Estacional Tarea Aditivo
library(forecast)
Modelo_HoltWinters <-
HoltWinters(Yt, seasonal = "additive", optim.start = c(.99, .99, .99))
Modelo_HoltWinters
## Holt-Winters exponential smoothing with trend and additive seasonal component.
##
## Call:
## HoltWinters(x = Yt, seasonal = "additive", optim.start = c(0.99, 0.99, 0.99))
##
## Smoothing parameters:
## alpha: 0.898148
## beta : 0
## gamma: 1
##
## Coefficients:
## [,1]
## a 116.4534518
## b 0.1600306
## s1 -4.0691412
## s2 3.1366975
## s3 4.4287527
## s4 -1.9042915
## s5 -0.4459172
## s6 -1.9969246
## s7 -3.7516231
## s8 2.1866170
## s9 9.7691060
## s10 -2.4926728
## s11 -4.6818571
## s12 0.5665482
Generación del pronostico
Pronostico<-forecast(Modelo_HoltWinters,h=12,level=c(.99))
Pronostico
## Point Forecast Lo 99 Hi 99
## Apr 2021 112.5443 105.59550 119.4932
## May 2021 119.9102 110.57010 129.2503
## Jun 2021 121.3623 110.12892 132.5957
## Jul 2021 115.1893 102.33860 128.0400
## Aug 2021 116.8077 102.52164 131.0937
## Sep 2021 115.4167 99.82689 131.0065
## Oct 2021 113.8220 97.02938 130.6147
## Nov 2021 119.9203 102.00538 137.8352
## Dec 2021 127.6628 108.69191 146.6338
## Jan 2022 115.5611 95.58993 135.5322
## Feb 2022 113.5319 92.60830 134.4556
## Mar 2022 118.9404 97.10577 140.7750
Pronostico %>% autoplot(main = "Pronostico IVAE, El Salvador 2009-2022[marzo]", xlab = "Años/Meses", ylab = "Indice")
Aproximación por Espacios de los Estados
Pronostico2<-hw(Yt, h = 12, level = c(.99), seasonal = "additive", initial = "optimal")
Pronostico2
## Point Forecast Lo 99 Hi 99
## Apr 2021 112.6516 106.12788 119.1753
## May 2021 118.9992 109.77306 128.2253
## Jun 2021 119.5224 108.22210 130.8227
## Jul 2021 114.1081 101.05877 127.1574
## Aug 2021 116.5251 101.93444 131.1157
## Sep 2021 115.2657 99.28135 131.2501
## Oct 2021 113.7033 96.43692 130.9697
## Nov 2021 119.6967 101.23673 138.1566
## Dec 2021 127.3059 107.72473 146.8871
## Jan 2022 115.6947 95.05274 136.3366
## Feb 2022 113.0874 91.43631 134.7385
## Mar 2022 120.1331 97.51756 142.7486
Pronostico2 %>% autoplot(main = "Pronostico IVAE, El Salvador 2009-2022[marzo]", xlab = "Años/Meses", ylab = "Indice")
Estimación del modelo de Holt Winters Estacional Tarea
Mutiplicativo
library(forecast)
Modelo_HoltWinters <-
HoltWinters(Yt, seasonal = "multiplicative", optim.start = c(.99, .99, .99))
Modelo_HoltWinters
## Holt-Winters exponential smoothing with trend and multiplicative seasonal component.
##
## Call:
## HoltWinters(x = Yt, seasonal = "multiplicative", optim.start = c(0.99, 0.99, 0.99))
##
## Smoothing parameters:
## alpha: 0.840818
## beta : 0
## gamma: 1
##
## Coefficients:
## [,1]
## a 117.0799398
## b 0.1600306
## s1 0.9502257
## s2 1.0233276
## s3 1.0518153
## s4 0.9900274
## s5 1.0007534
## s6 0.9807087
## s7 0.9600206
## s8 1.0149629
## s9 1.0915425
## s10 0.9796753
## s11 0.9584675
## s12 0.9994880
Generación del pronostico
Pronostico<-forecast(Modelo_HoltWinters,h=12,level=c(.99))
Pronostico
## Point Forecast Lo 99 Hi 99
## Apr 2021 111.4044 104.23548 118.5734
## May 2021 120.1387 110.46738 129.8099
## Jun 2021 123.6514 112.06275 135.2401
## Jul 2021 116.5461 104.01475 129.0774
## Aug 2021 117.9689 103.95243 131.9854
## Sep 2021 115.7630 100.74293 130.7830
## Oct 2021 113.4746 97.56399 129.3852
## Nov 2021 120.1312 102.31232 137.9501
## Dec 2021 129.3699 109.33965 149.4001
## Jan 2022 116.2681 97.23000 135.3062
## Feb 2022 113.9045 94.31102 133.4981
## Mar 2022 118.9394 69.88995 167.9888
Pronostico %>% autoplot(main = "Pronostico IVAE, El Salvador 2009-2022[marzo]", xlab = "Años/Meses", ylab = "Indice")
Aproximación por Espacios de los Estados
Pronostico2<-hw(Yt, h = 12, level = c(.99), seasonal = "multiplicative", initial = "optimal")
Pronostico2
## Point Forecast Lo 99 Hi 99
## Apr 2021 107.4928 97.11173 117.8738
## May 2021 115.3370 104.19432 126.4796
## Jun 2021 115.6946 104.51281 126.8764
## Jul 2021 109.6301 99.02943 120.2308
## Aug 2021 112.2047 101.34932 123.0600
## Sep 2021 110.2284 99.55799 120.8989
## Oct 2021 107.9393 97.48358 118.3950
## Nov 2021 114.4306 103.33807 125.5231
## Dec 2021 122.6459 110.74752 134.5442
## Jan 2022 108.5830 98.03978 119.1262
## Feb 2022 107.1239 96.71251 117.5353
## Mar 2022 113.1678 102.15770 124.1780
Pronostico2 %>% autoplot(main = "Pronostico IVAE, El Salvador 2009-2022[marzo]", xlab = "Años/Meses", ylab = "Indice")
