Importacion del modelo
library(readxl)
library(tidyr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(forecast)
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
IVAE_03_22<-read_excel("C:\\Users\\MOLINA\\OneDrive\\Escritorio\\ARCHIVOS DE R\\IVAE_03_22.xls.xlsx",col_names = FALSE,skip = 6,n_max = 10)
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data.ivae<-pivot_longer(data = IVAE_03_22[1,],names_to = "vars",cols = 2:160,values_to = "indice") %>% select("indice")
serie.ivae.ts<-data.ivae %>% ts(start = c(2009,1),frequency = 12)
Yt <- serie.ivae.ts
serie.ivae.ts %>%
autoplot(main="IVAE ENERO 2009- MARZ0 2022",
xlab="Años/meses",
ylab="Indice")
PRONOSTICO DE SERIES TEMPORALES, ENFOQUE DETERMINISTICO
(CLASICO)
Pronostico modelo de Holt Winters
Usando Stats y Forecast
Multiplicativo
library(forecast)
#Estimar el modelo
modelohw<-HoltWinters(x = Yt,
seasonal= "multiplicative",
optim.start=c(0.9,0.9,0.9)) #OPCIONAL:BUSCA establecer los valores quinceales para la busqueda de parametros que busca HW
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 pronostico
pronosticoshw<-forecast(object = modelohw,h = 12,level = c(0.95))
pronosticoshw
## Point Forecast Lo 95 Hi 95
## Apr 2022 112.9503 107.46492 118.4358
## May 2022 120.9752 113.57246 128.3779
## Jun 2022 124.0929 115.22236 132.9634
## Jul 2022 118.5640 108.86876 128.2592
## Aug 2022 119.2908 108.50116 130.0804
## Sep 2022 117.4038 105.81297 128.9947
## Oct 2022 114.1680 101.97016 126.3657
## Nov 2022 121.3911 107.66977 135.1125
## Dec 2022 130.2643 114.88360 145.6450
## Jan 2023 116.9603 102.34981 131.5708
## Feb 2023 115.6485 100.48405 130.8129
## Mar 2023 121.0126 83.19827 158.8270
#Grafico de la serie original t del pronostico
pronosticoshw %>% autoplot(main="Pronostco HW multiplicativo", xlab="Años/meses")
Aditivo
library(forecast)
#Estimar el modelo
modelohw.ad<-HoltWinters(x = Yt,
seasonal= "additive",
optim.start=c(0.9,0.9,0.9)) #OPCIONAL:bUSCA establecer los valores quinceales para la busqueda de parametros que busca HW
modelohw.ad
## 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 pronostico
pronosticoshw.ad<-forecast(object = modelohw.ad,h = 12,level = c(0.95))
pronosticoshw.ad
## Point Forecast Lo 95 Hi 95
## Apr 2022 114.3507 109.10096 119.6005
## May 2022 121.4608 114.34766 128.5740
## Jun 2022 122.5014 113.92039 131.0825
## Jul 2022 117.1302 107.29801 126.9623
## Aug 2022 118.3156 107.37443 129.2567
## Sep 2022 117.2255 105.27790 129.1731
## Oct 2022 115.1336 102.25799 128.0093
## Nov 2022 121.6237 107.88248 135.3648
## Dec 2022 129.1623 114.60695 143.7176
## Jan 2023 116.8985 101.57229 132.2248
## Feb 2023 115.3991 99.33889 131.4594
## Mar 2023 121.0011 104.23903 137.7632
#Grafico de la serie original y del pronostico
pronosticoshw.ad %>% autoplot(main="Pronostco HW aditivo", xlab="Años/meses")
Usando forecast(aproximacion por espacios de los estados ETS)
Multiplicativo
library(forecast)
#generar el pronostico
pronosticoshw2_mul<-hw(y = Yt,
h = 12,
level = c(0.95),
seasonal = "multiplicative",
initial = "optimal")
pronosticoshw2_mul
## Point Forecast Lo 95 Hi 95
## Apr 2022 111.9184 105.09260 118.7442
## May 2022 119.3664 110.67455 128.0583
## Jun 2022 122.8422 112.63807 133.0464
## Jul 2022 118.4498 107.52066 129.3789
## Aug 2022 120.0189 107.93209 132.1056
## Sep 2022 118.4260 105.56932 131.2828
## Oct 2022 114.3087 101.05402 127.5633
## Nov 2022 120.2837 105.49291 135.0745
## Dec 2022 127.9776 111.38423 144.5709
## Jan 2023 114.4558 98.88074 130.0309
## Feb 2023 113.5923 97.43199 129.7527
## Mar 2023 119.3883 101.68918 137.0873
#grafico de la serie original y del pronostico
pronosticoshw2_mul %>% autoplot(main ="Pronostico aproximacion por espacios multiplicativo",xlab="Años/meses")
Aditivo
library(forecast)
#Estimar el modelo
modelohw.ad2<-HoltWinters(x = Yt,
seasonal= "additive",
optim.start=c(0.9,0.9,0.9))
modelohw.ad2
## 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 modelo
pronosticoshw.ad.2<-forecast(object = modelohw.ad,h = 12,level = c(0.95))
pronosticoshw.ad.2
## Point Forecast Lo 95 Hi 95
## Apr 2022 114.3507 109.10096 119.6005
## May 2022 121.4608 114.34766 128.5740
## Jun 2022 122.5014 113.92039 131.0825
## Jul 2022 117.1302 107.29801 126.9623
## Aug 2022 118.3156 107.37443 129.2567
## Sep 2022 117.2255 105.27790 129.1731
## Oct 2022 115.1336 102.25799 128.0093
## Nov 2022 121.6237 107.88248 135.3648
## Dec 2022 129.1623 114.60695 143.7176
## Jan 2023 116.8985 101.57229 132.2248
## Feb 2023 115.3991 99.33889 131.4594
## Mar 2023 121.0011 104.23903 137.7632
#Grafico de la serie original y del pronostico
pronosticoshw.ad.2 %>% autoplot(main="Pronostco HW aditivo", xlab="Años/meses")