1. importar los datos
library(ggplot2)
library(forecast)
library(readxl)
ivae_ts <- read_excel("C:/Users/carlo/Desktop/ivae_ts2.xlsx",)
data=ivae_ts$IVAE %>% ts(start = c(2005,1),frequency = 12)->ivae
print(ivae)
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 2005 75.92 75.76 81.87 80.58 88.91 88.37 83.28 84.43 86.20 84.28
## 2006 81.72 80.03 87.53 80.37 90.05 90.58 83.50 86.67 91.30 92.21
## 2007 83.31 77.77 86.95 78.74 93.62 92.29 87.98 91.63 88.35 92.64
## 2008 85.76 85.50 87.67 89.96 93.86 92.26 86.03 91.05 92.21 93.62
## 2009 86.73 80.85 87.19 83.92 91.42 93.46 86.39 86.72 87.57 85.27
## 2010 85.56 84.69 90.90 85.94 94.33 92.23 87.19 90.25 88.99 88.73
## 2011 90.26 86.73 94.33 90.80 98.49 97.59 92.16 94.22 92.34 89.07
## 2012 92.66 91.20 98.45 91.22 102.83 102.84 93.61 98.21 93.94 93.49
## 2013 95.67 90.77 96.12 96.34 103.08 101.58 96.42 98.96 97.74 96.22
## 2014 98.70 94.70 101.29 97.12 103.86 104.73 98.48 98.60 98.25 96.43
## 2015 98.88 94.83 103.16 98.76 105.66 105.46 101.68 101.06 100.63 100.43
## 2016 99.21 97.71 102.52 103.37 107.71 110.69 103.99 106.24 104.85 102.09
## 2017 101.62 99.20 108.68 101.61 111.02 113.76 105.64 107.88 106.01 102.76
## 2018 105.30 103.75 109.16 107.74 112.62 114.34 108.16 111.08 106.38 105.39
## 2019 107.37 106.47 112.71 109.23 115.45 115.61 110.99 113.05 110.86 108.32
## 2020 110.53 111.38 106.03 87.69 89.85 97.23 95.87 101.65 105.08 104.28
## Nov Dec
## 2005 90.61 94.25
## 2006 96.98 94.23
## 2007 97.99 101.28
## 2008 95.41 94.03
## 2009 91.86 99.64
## 2010 93.12 100.76
## 2011 96.87 103.90
## 2012 99.61 105.05
## 2013 101.24 108.36
## 2014 100.65 107.20
## 2015 104.88 109.82
## 2016 106.57 115.13
## 2017 109.94 117.41
## 2018 111.65 119.85
## 2019 116.24 123.44
## 2020
autoplot(ivae,xlab = "años",ylab = "Indice",main = "IVAE total, periodo 2005-2020 (octubre)")+theme_bw()
2. proyección a Seis meses
library(forecast)
modelo<-auto.arima(y = ivae)
summary(modelo)
## Series: ivae
## ARIMA(1,0,2)(0,1,1)[12] with drift
##
## Coefficients:
## ar1 ma1 ma2 sma1 drift
## 0.6723 0.0795 0.1863 -0.7499 0.1400
## s.e. 0.0879 0.1045 0.0968 0.0772 0.0205
##
## sigma^2 estimated as 7.362: log likelihood=-433.12
## AIC=878.25 AICc=878.74 BIC=897.34
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.0005216099 2.589122 1.768711 -0.06752556 1.847341 0.5402127
## ACF1
## Training set 0.009341845
pronosticos<-forecast(modelo,h = 6)
autoplot(pronosticos)+xlab("Años")+ylab("indice")+theme_bw()
library(forecast)
autoplot(pronosticos$x,series = "IVAE")+autolayer(pronosticos$fitted,series = "Pronóstico")+ggtitle("Ajuste SARIMA")
3. Serie ampliada
ivae_h<-ts(as.numeric(rbind(as.matrix(pronosticos$x),as.matrix(pronosticos$mean))),start = c(2005,1),frequency = 12)
print(ivae_h)
## Jan Feb Mar Apr May Jun Jul Aug
## 2005 75.9200 75.7600 81.8700 80.5800 88.9100 88.3700 83.2800 84.4300
## 2006 81.7200 80.0300 87.5300 80.3700 90.0500 90.5800 83.5000 86.6700
## 2007 83.3100 77.7700 86.9500 78.7400 93.6200 92.2900 87.9800 91.6300
## 2008 85.7600 85.5000 87.6700 89.9600 93.8600 92.2600 86.0300 91.0500
## 2009 86.7300 80.8500 87.1900 83.9200 91.4200 93.4600 86.3900 86.7200
## 2010 85.5600 84.6900 90.9000 85.9400 94.3300 92.2300 87.1900 90.2500
## 2011 90.2600 86.7300 94.3300 90.8000 98.4900 97.5900 92.1600 94.2200
## 2012 92.6600 91.2000 98.4500 91.2200 102.8300 102.8400 93.6100 98.2100
## 2013 95.6700 90.7700 96.1200 96.3400 103.0800 101.5800 96.4200 98.9600
## 2014 98.7000 94.7000 101.2900 97.1200 103.8600 104.7300 98.4800 98.6000
## 2015 98.8800 94.8300 103.1600 98.7600 105.6600 105.4600 101.6800 101.0600
## 2016 99.2100 97.7100 102.5200 103.3700 107.7100 110.6900 103.9900 106.2400
## 2017 101.6200 99.2000 108.6800 101.6100 111.0200 113.7600 105.6400 107.8800
## 2018 105.3000 103.7500 109.1600 107.7400 112.6200 114.3400 108.1600 111.0800
## 2019 107.3700 106.4700 112.7100 109.2300 115.4500 115.6100 110.9900 113.0500
## 2020 110.5300 111.3800 106.0300 87.6900 89.8500 97.2300 95.8700 101.6500
## 2021 108.7177 107.7527 111.6328 104.8025
## Sep Oct Nov Dec
## 2005 86.2000 84.2800 90.6100 94.2500
## 2006 91.3000 92.2100 96.9800 94.2300
## 2007 88.3500 92.6400 97.9900 101.2800
## 2008 92.2100 93.6200 95.4100 94.0300
## 2009 87.5700 85.2700 91.8600 99.6400
## 2010 88.9900 88.7300 93.1200 100.7600
## 2011 92.3400 89.0700 96.8700 103.9000
## 2012 93.9400 93.4900 99.6100 105.0500
## 2013 97.7400 96.2200 101.2400 108.3600
## 2014 98.2500 96.4300 100.6500 107.2000
## 2015 100.6300 100.4300 104.8800 109.8200
## 2016 104.8500 102.0900 106.5700 115.1300
## 2017 106.0100 102.7600 109.9400 117.4100
## 2018 106.3800 105.3900 111.6500 119.8500
## 2019 110.8600 108.3200 116.2400 123.4400
## 2020 105.0800 104.2800 112.2728 120.0459
## 2021
4. Descomposición de la serie temporal
library(stats)
fit<-stl(ivae_h,"periodic")
autoplot(fit)+theme_bw()
TC<-fit$time.series[,2]
print(TC)
## Jan Feb Mar Apr May Jun Jul
## 2005 81.68300 82.20308 82.72317 83.20293 83.68268 84.14033 84.59797
## 2006 86.10761 86.32076 86.53391 86.94375 87.35358 87.68576 88.01794
## 2007 88.22274 88.28052 88.33829 88.51946 88.70062 89.14859 89.59656
## 2008 91.22212 91.16829 91.11446 91.07529 91.03612 90.92730 90.81849
## 2009 89.47014 89.17449 88.87884 88.60650 88.33417 88.35138 88.36858
## 2010 89.40749 89.58019 89.75288 89.91342 90.07397 90.23566 90.39734
## 2011 92.45009 92.82840 93.20672 93.44539 93.68406 93.86563 94.04721
## 2012 95.63108 95.98330 96.33553 96.59006 96.84458 96.95572 97.06686
## 2013 97.37757 97.60117 97.82478 98.05542 98.28607 98.51201 98.73795
## 2014 99.98197 100.11160 100.24124 100.23659 100.23193 100.14478 100.05762
## 2015 100.63081 100.93423 101.23764 101.53399 101.83034 102.00267 102.17500
## 2016 103.28678 103.67162 104.05646 104.38950 104.72255 104.96757 105.21259
## 2017 106.12693 106.35014 106.57335 106.78253 106.99170 107.19257 107.39344
## 2018 108.63464 108.88610 109.13756 109.32673 109.51590 109.63467 109.75344
## 2019 110.87674 111.19037 111.50400 111.81467 112.12535 112.36168 112.59802
## 2020 107.20467 105.90932 104.61397 103.84401 103.07405 102.82086 102.56766
## 2021 109.29216 110.61179 111.93143 113.27528
## Aug Sep Oct Nov Dec
## 2005 85.03256 85.46716 85.69144 85.91572 86.01166
## 2006 88.08771 88.15748 88.13550 88.11353 88.16813
## 2007 90.07807 90.55959 90.85473 91.14986 91.18599
## 2008 90.61055 90.40262 90.15151 89.90040 89.68527
## 2009 88.54895 88.72932 88.91527 89.10122 89.25436
## 2010 90.63009 90.86283 91.21965 91.57647 92.01328
## 2011 94.24041 94.43362 94.68985 94.94609 95.28858
## 2012 97.06734 97.06782 97.10220 97.13658 97.25707
## 2013 98.96724 99.19654 99.40497 99.61340 99.79769
## 2014 100.02140 99.98518 100.08626 100.18735 100.40908
## 2015 102.25013 102.32527 102.48845 102.65163 102.96920
## 2016 105.37021 105.52783 105.64707 105.76632 105.94663
## 2017 107.59065 107.78787 107.98363 108.17940 108.40702
## 2018 109.88719 110.02094 110.20055 110.38015 110.62845
## 2019 112.48856 112.37909 111.35176 110.32442 108.76455
## 2020 103.60683 104.64599 105.73800 106.83000 108.06108
## 2021
5. Cálculo de las tasas
library(dplyr)
library(zoo)
TC %>% as.numeric() %>% as.data.frame()->TC_df
names(TC_df)<-c("TC")
TC_df %>% mutate(T_1_1=(TC/dplyr::lag(TC,n=1)-1)*100,
T_1_12=(TC/dplyr::lag(TC,n=12)-1)*100,
T_12_12=(rollapply(TC,12,mean,align='right',fill=NA)
/rollapply(dplyr::lag(TC,n=12),12,mean,align='right',fill=NA)-1)*100) %>%
#Aquí se realiza el centrado
mutate(T_1_12C=dplyr::lead(T_1_12,n = 6),
T_12_12C=dplyr::lead(T_12_12,n = 12)) %>% ts(start = c(2005,1),frequency = 12)->tabla_coyuntura
print(tail(tabla_coyuntura,n=12))
## TC T_1_1 T_1_12 T_12_12 T_1_12C T_12_12C
## May 2020 103.0741 -0.7414566 -8.0724759 -1.739619 -3.1674078 NA
## Jun 2020 102.8209 -0.2456459 -8.4911746 -2.657933 -0.6467786 NA
## Jul 2020 102.5677 -0.2462508 -8.9081157 -3.617699 1.9472032 NA
## Aug 2020 103.6068 1.0131524 -7.8956743 -4.470047 4.4400947 NA
## Sep 2020 104.6460 1.0029906 -6.8812605 -5.216068 6.9947210 NA
## Oct 2020 105.7380 1.0435212 -5.0414662 -5.716548 9.0821471 NA
## Nov 2020 106.8300 1.0327443 -3.1674078 -5.973473 NA NA
## Dec 2020 108.0611 1.1523747 -0.6467786 -5.895052 NA NA
## Jan 2021 109.2922 1.1392463 1.9472032 -5.479561 NA NA
## Feb 2021 110.6118 1.2074347 4.4400947 -4.750034 NA NA
## Mar 2021 111.9314 1.1930296 6.9947210 -3.700043 NA NA
## Apr 2021 113.2753 1.2006032 9.0821471 -2.398105 NA NA
6. Gráfico de las tasas (centradas)
library(dplyr)
library(forecast)
library(ggplot2)
tabla_coyuntura %>% as.data.frame() %>% select(T_1_12C,T_12_12C) %>% ts(start = c(2005,1),frequency = 12)->tabla_coyuntura_graficos
autoplot(tabla_coyuntura_graficos)+theme_bw()
tabla_coyuntura %>% as.data.frame() %>% select(T_1_1) %>% ts(start = c(2005,1),frequency = 12) %>% autoplot()
