“Pronóstico de series temporales, enfoque deterministico (clásico)”
NATHALY EUNICE ORELLANA ALVARADO OA18024
2022-06-23
Cargamos la base de datos
library(readxl)
library(forecast)
IVAE_SLV<- read_excel("F:/practica pronostico de series temporales/IVAE_SLV.xlsx",
col_types = c("skip", "numeric"),
skip = 0)
#serie
serie.ivae.ts<- ts(data = IVAE_SLV, start = c(2009,1),
frequency = 12)
Yt<-serie.ivae.tsI. Pronóstico de series temporales, enfoque deterministico (clasico) (MULTIPLICATIVA)
1. Pronóstico Modelo de Holt Winters
1.1. Usando Stars y forecast
library(forecast)
#Estimar el modelo
ModeloHW<-HoltWinters(x = Yt,
seasonal = "multiplicative", #Tipo de componente estacional
optim.start = c(0.9,0.9,0.9))#permite establecer los valores inicales para la busqueda de parametros
ModeloHW## 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: 0.8472467
## beta : 0
## gamma: 1
##
## Coefficients:
## [,1]
## a 118.3719784
## b 0.1600947
## s1 0.9529095
## s2 1.0192349
## s3 1.0440936
## s4 0.9962326
## s5 1.0009929
## s6 0.9838373
## s7 0.9554392
## s8 1.0145286
## s9 1.0872315
## s10 0.9748891
## s11 0.9626701
## s12 1.0059813#Generar el pronóstico:
PronosticoHW<-forecast(ModeloHW,h= 12,level=c(.95,0.99))
PronosticoHW## Point Forecast Lo 95 Hi 95 Lo 99 Hi 99
## Apr 2022 112.9503 107.46492 118.4358 105.74127 120.1594
## May 2022 120.9752 113.57246 128.3779 111.24635 130.7040
## Jun 2022 124.0929 115.22236 132.9634 112.43504 135.7507
## Jul 2022 118.5640 108.86876 128.2592 105.82229 131.3057
## Aug 2022 119.2908 108.50116 130.0804 105.11081 133.4708
## Sep 2022 117.4038 105.81297 128.9947 102.17086 132.6368
## Oct 2022 114.1680 101.97016 126.3657 98.13733 130.1986
## Nov 2022 121.3911 107.66977 135.1125 103.35822 139.4240
## Dec 2022 130.2643 114.88360 145.6450 110.05064 150.4779
## Jan 2023 116.9603 102.34981 131.5708 97.75887 136.1617
## Feb 2023 115.6485 100.48405 130.8129 95.71905 135.5779
## Mar 2023 121.0126 83.19827 158.8270 71.31613 170.7091#Gráfico de la serie original y del pronóstico.
PronosticoHW %>% autoplot(main = "Pronostico IVAE, El Salvador 2009-2022[marzo]",
xlab = "Años/Meses",
ylab = "Indice")
1.2. Usando Forecast (Aproximación por Espacios de los Estados ETS)
library(forecast)
#Generar el pronostico:
PronosticosMW_2<-hw(y = Yt,
h= 12,
level = c(0.95,0.99),
seasonal = "multiplicative",
initial = "optimal")
PronosticosMW_2## Point Forecast Lo 95 Hi 95 Lo 99 Hi 99
## Apr 2022 111.9184 105.09260 118.7442 102.94778 120.8891
## May 2022 119.3664 110.67455 128.0583 107.94336 130.7895
## Jun 2022 122.8422 112.63807 133.0464 109.43169 136.2528
## Jul 2022 118.4498 107.52066 129.3789 104.08648 132.8131
## Aug 2022 120.0189 107.93209 132.1056 104.13415 135.9036
## Sep 2022 118.4260 105.56932 131.2828 101.52944 135.3226
## Oct 2022 114.3087 101.05402 127.5633 96.88911 131.7282
## Nov 2022 120.2837 105.49291 135.0745 100.84531 139.7221
## Dec 2022 127.9776 111.38423 144.5709 106.17022 149.7849
## Jan 2023 114.4558 98.88074 130.0309 93.98669 134.9249
## Feb 2023 113.5923 97.43199 129.7527 92.35405 134.8306
## Mar 2023 119.3883 101.68918 137.0873 96.12773 142.6488#Gráfico de la serie original y del pronóstico
PronosticosMW_2 %>% autoplot(main = "Pronostico IVAE, El Salvador 2009-2022[marzo]",
xlab = "Años/Meses",
ylab = "Indice")
II. Pronóstico de series temporales, enfoque deterministico (clasico)(ADITIVA)
1. Pronóstico Modelo de Holt Winters
1.1. Usando Stars y forecast
library(forecast)
#Estimar el modelo
ModeloHW<-HoltWinters(x = Yt,
seasonal = "additive",
optim.start = c(0.9,0.9,0.9))
ModeloHW## Holt-Winters exponential smoothing with trend and additive seasonal component.
##
## Call:
## HoltWinters(x = Yt, seasonal = "additive", optim.start = c(0.9, 0.9, 0.9))
##
## Smoothing parameters:
## alpha: 0.9142733
## beta : 0
## gamma: 1
##
## Coefficients:
## [,1]
## a 118.0938359
## b 0.1600947
## s1 -3.9032233
## s2 3.0467916
## s3 3.9273013
## s4 -1.6040517
## s5 -0.5787492
## s6 -1.8288955
## s7 -4.0808499
## s8 2.2490593
## s9 9.6275698
## s10 -2.7962367
## s11 -4.4557494
## s12 0.9861641#Generar el pronóstico:
PronosticosMWa<-forecast(object = ModeloHW,h=12, level=c(0.95,0.99))
PronosticosMWa## Point Forecast Lo 95 Hi 95 Lo 99 Hi 99
## Apr 2022 114.3507 109.10096 119.6005 107.45137 121.2500
## May 2022 121.4608 114.34766 128.5740 112.11255 130.8091
## Jun 2022 122.5014 113.92039 131.0825 111.22404 133.7788
## Jul 2022 117.1302 107.29801 126.9623 104.20853 130.0518
## Aug 2022 118.3156 107.37443 129.2567 103.93648 132.6946
## Sep 2022 117.2255 105.27790 129.1731 101.52368 132.9273
## Oct 2022 115.1336 102.25799 128.0093 98.21216 132.0551
## Nov 2022 121.6237 107.88248 135.3648 103.56470 139.6826
## Dec 2022 129.1623 114.60695 143.7176 110.03335 148.2912
## Jan 2023 116.8985 101.57229 132.2248 96.75644 137.0407
## Feb 2023 115.3991 99.33889 131.4594 94.29240 136.5059
## Mar 2023 121.0011 104.23903 137.7632 98.97199 143.0303#Gráfico de la serie original y del pronóstico.
PronosticosMWa %>% autoplot(main = "Pronostico IVAE, El Salvador 2009-2022[marzo]",
xlab = "Años/Meses",
ylab = "Indice")
1.2. Usando Forecast (Aproximacion por Espacios de los Estados ETS)
library(forecast)
#Generar el pronóstico:
PronosticosMWa_2<-hw(y = Yt,
h= 12,
level = c(0.95,0.99),
seasonal = "additive",
initial = "optimal")
PronosticosMWa_2## Point Forecast Lo 95 Hi 95 Lo 99 Hi 99
## Apr 2022 114.4467 109.5770 119.3163 108.04686 120.8465
## May 2022 120.9088 114.0222 127.7955 111.85827 129.9594
## Jun 2022 121.6963 113.2617 130.1309 110.61138 132.7813
## Jul 2022 116.4405 106.7007 126.1803 103.64021 129.2408
## Aug 2022 119.0528 108.1629 129.9428 104.74100 133.3646
## Sep 2022 118.3286 106.3987 130.2585 102.65006 134.0071
## Oct 2022 116.5899 103.7035 129.4763 99.65435 133.5255
## Nov 2022 122.4772 108.7004 136.2539 104.37143 140.5829
## Dec 2022 129.5220 114.9088 144.1352 110.31701 148.7270
## Jan 2023 117.7994 102.3950 133.2038 97.55460 138.0443
## Feb 2023 115.4411 99.2840 131.5982 94.20707 136.6751
## Mar 2023 121.4513 104.5750 138.3277 99.27204 143.6306#Gráfico de la serie original y del pronóstico
PronosticosMWa_2 %>% autoplot(main = "Pronostico IVAE, El Salvador 2009-2022[marzo]",
xlab = "Años/Meses",
ylab = "Indice")
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