Agricultura, Ganaderia, Silvicultura y Pesca
JOSE RICARDO CORTEZ MELGAR
21 de noviembre de 2019
ImportaciĆ³n de datos
library(ggplot2)
library(forecast)
library(readxl)
AGSP_ts <- read_excel("C:/Users/corte/Desktop/Agricultura, ganaderia, silvicultura y pesca.xlsx",
col_types = c("skip", "skip", "numeric"))
data=AGSP_ts%>%ts(start = c(2005,1),frequency = 12)->AGSP
print(AGSP)## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 2005 92.64 68.41 60.10 65.43 137.50 141.97 71.05 128.73 97.27 71.26
## 2006 86.67 69.31 62.80 65.16 136.68 143.43 70.70 137.40 107.81 74.25
## 2007 94.30 73.70 65.83 69.07 146.57 156.23 72.82 153.89 117.05 76.11
## 2008 91.09 70.96 64.06 67.95 150.52 165.58 75.11 154.51 114.93 74.25
## 2009 86.82 70.17 65.24 67.36 137.69 153.16 75.56 136.56 109.59 74.07
## 2010 97.58 82.25 68.26 68.26 153.40 149.50 75.17 147.21 103.37 74.22
## 2011 92.58 68.70 68.64 68.33 145.21 149.11 69.83 131.08 101.34 75.92
## 2012 88.55 62.62 61.39 65.20 149.91 183.43 71.85 146.35 116.73 73.90
## 2013 90.05 66.16 62.83 63.18 124.26 164.76 68.33 125.32 117.47 72.17
## 2014 97.83 75.14 61.11 67.20 122.92 154.11 74.61 124.65 118.20 74.18
## 2015 91.59 67.18 66.22 69.67 112.99 142.87 73.00 116.07 112.53 71.90
## 2016 82.95 70.20 66.91 66.61 118.81 168.59 74.25 132.16 126.73 75.35
## 2017 87.09 64.26 70.53 63.94 125.82 178.89 76.25 125.83 121.14 73.12
## 2018 94.24 71.82 71.19 68.57 127.30 164.72 71.66 126.25 116.33 71.38
## 2019 92.86 72.60 70.75 68.58 128.30 168.88 75.16 130.35
## Nov Dec
## 2005 115.84 123.22
## 2006 124.54 127.28
## 2007 131.05 135.13
## 2008 127.42 132.96
## 2009 122.34 122.11
## 2010 125.23 137.40
## 2011 126.99 130.58
## 2012 131.53 130.47
## 2013 124.56 110.55
## 2014 124.93 105.12
## 2015 112.19 97.74
## 2016 123.86 104.69
## 2017 119.79 106.43
## 2018 121.10 108.96
## 2019autoplot(AGSP,xlab = "aƱos",ylab = "Indice",main = "Agricultura, ganaderĆa, silvicultura y pesca total, periodo 2005-2019 (agosto)")+theme_bw()
2. ProyecciĆ³n a 6 meses
library(forecast)
modelo<-auto.arima(y= AGSP)
summary(modelo)## Series: AGSP
## ARIMA(3,0,2)(0,1,2)[12]
##
## Coefficients:
## ar1 ar2 ar3 ma1 ma2 sma1 sma2
## 0.0965 -0.5711 0.4537 0.2132 0.7138 -0.2316 -0.1690
## s.e. 0.1355 0.1735 0.0780 0.1591 0.1968 0.0945 0.0665
##
## sigma^2 estimated as 47.77: log likelihood=-547.45
## AIC=1110.91 AICc=1111.84 BIC=1135.71
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.2114352 6.527879 4.453522 0.007816642 4.269373 0.7750268
## ACF1
## Training set -0.02784543pronosticos<-forecast(modelo,h = 6)
autoplot(pronosticos)+xlab("AƱos")+ylab("indice")+theme_bw()
library(forecast)
autoplot(pronosticos$x,series = "AGSP")+autolayer(pronosticos$fitted,series = "PronĆ³stico")+ggtitle("Ajustes ARIMA")
##3. Serie Ampliada
AGSP_h<-ts(as.numeric(rbind(as.matrix(pronosticos$x),as.matrix(pronosticos$mean))),start = c(2005,1),frequency = 12)
print(AGSP_h)## Jan Feb Mar Apr May Jun Jul
## 2005 92.64000 68.41000 60.10000 65.43000 137.50000 141.97000 71.05000
## 2006 86.67000 69.31000 62.80000 65.16000 136.68000 143.43000 70.70000
## 2007 94.30000 73.70000 65.83000 69.07000 146.57000 156.23000 72.82000
## 2008 91.09000 70.96000 64.06000 67.95000 150.52000 165.58000 75.11000
## 2009 86.82000 70.17000 65.24000 67.36000 137.69000 153.16000 75.56000
## 2010 97.58000 82.25000 68.26000 68.26000 153.40000 149.50000 75.17000
## 2011 92.58000 68.70000 68.64000 68.33000 145.21000 149.11000 69.83000
## 2012 88.55000 62.62000 61.39000 65.20000 149.91000 183.43000 71.85000
## 2013 90.05000 66.16000 62.83000 63.18000 124.26000 164.76000 68.33000
## 2014 97.83000 75.14000 61.11000 67.20000 122.92000 154.11000 74.61000
## 2015 91.59000 67.18000 66.22000 69.67000 112.99000 142.87000 73.00000
## 2016 82.95000 70.20000 66.91000 66.61000 118.81000 168.59000 74.25000
## 2017 87.09000 64.26000 70.53000 63.94000 125.82000 178.89000 76.25000
## 2018 94.24000 71.82000 71.19000 68.57000 127.30000 164.72000 71.66000
## 2019 92.86000 72.60000 70.75000 68.58000 128.30000 168.88000 75.16000
## 2020 92.14539 71.36930
## Aug Sep Oct Nov Dec
## 2005 128.73000 97.27000 71.26000 115.84000 123.22000
## 2006 137.40000 107.81000 74.25000 124.54000 127.28000
## 2007 153.89000 117.05000 76.11000 131.05000 135.13000
## 2008 154.51000 114.93000 74.25000 127.42000 132.96000
## 2009 136.56000 109.59000 74.07000 122.34000 122.11000
## 2010 147.21000 103.37000 74.22000 125.23000 137.40000
## 2011 131.08000 101.34000 75.92000 126.99000 130.58000
## 2012 146.35000 116.73000 73.90000 131.53000 130.47000
## 2013 125.32000 117.47000 72.17000 124.56000 110.55000
## 2014 124.65000 118.20000 74.18000 124.93000 105.12000
## 2015 116.07000 112.53000 71.90000 112.19000 97.74000
## 2016 132.16000 126.73000 75.35000 123.86000 104.69000
## 2017 125.83000 121.14000 73.12000 119.79000 106.43000
## 2018 126.25000 116.33000 71.38000 121.10000 108.96000
## 2019 130.35000 119.66077 73.50794 121.96594 108.45628
## 20204. DescompisiciĆ³n de la serie temporal
library(stats)
fit<-stl(AGSP_h,"periodic")
autoplot(fit)+theme_bw()
TC<-fit$time.series[,2]
print(TC)## Jan Feb Mar Apr May Jun Jul
## 2005 99.31823 98.97319 98.62815 98.37010 98.11206 97.92727 97.74249
## 2006 98.05934 98.43933 98.81931 99.23012 99.64094 100.07666 100.51238
## 2007 104.27907 104.96795 105.65683 106.17434 106.69184 107.01413 107.33643
## 2008 107.69422 107.78760 107.88098 107.82439 107.76779 107.70003 107.63227
## 2009 104.79511 104.12703 103.45896 103.00800 102.55705 102.50579 102.45453
## 2010 105.32045 105.53317 105.74590 105.80154 105.85718 105.83634 105.81551
## 2011 104.40025 103.89908 103.39790 102.94133 102.48475 102.13596 101.78716
## 2012 103.45441 104.54204 105.62967 106.21380 106.79793 107.14337 107.48880
## 2013 103.35508 102.47530 101.59553 100.99342 100.39131 100.12850 99.86569
## 2014 100.42488 100.33214 100.23940 100.15475 100.07009 99.83078 99.59147
## 2015 97.98196 97.29144 96.60091 95.99319 95.38547 94.78547 94.18547
## 2016 96.17314 97.15074 98.12834 99.13780 100.14727 100.71282 101.27836
## 2017 101.52714 101.50601 101.48488 101.51964 101.55440 101.73492 101.91544
## 2018 101.64461 101.45489 101.26516 101.21669 101.16822 101.08938 101.01054
## 2019 101.56572 101.92965 102.29359 102.54130 102.78902 102.54327 102.29751
## 2020 100.90378 100.58453
## Aug Sep Oct Nov Dec
## 2005 97.58665 97.43082 97.48009 97.52935 97.79435
## 2006 101.02280 101.53322 102.18125 102.82928 103.55417
## 2007 107.37626 107.41609 107.43344 107.45079 107.57251
## 2008 107.49096 107.34964 106.84811 106.34659 105.57085
## 2009 102.89013 103.32573 103.89580 104.46586 104.89316
## 2010 105.59466 105.37381 105.17946 104.98512 104.69268
## 2011 101.47414 101.16111 101.31405 101.46700 102.46070
## 2012 107.51317 107.53754 106.69779 105.85803 104.60655
## 2013 100.09565 100.32560 100.47819 100.63077 100.52783
## 2014 99.42895 99.26644 99.12448 98.98252 98.48224
## 2015 93.96094 93.73641 94.09920 94.46199 95.31757
## 2016 101.31370 101.34904 101.32645 101.30385 101.41550
## 2017 102.09354 102.27164 102.21950 102.16737 101.90599
## 2018 100.91503 100.81953 100.89751 100.97550 101.27061
## 2019 102.10919 101.92086 101.69698 101.47309 101.18844
## 20205. Calculo 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) %>%
#AquĆ 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
## Mar 2019 102.2936 0.3570470 1.01558325 -0.53981122 1.09238238 NA
## Apr 2019 102.5413 0.2421618 1.30869229 -0.40669621 0.79235201 NA
## May 2019 102.7890 0.2415768 1.60208218 -0.24246155 0.49278507 NA
## Jun 2019 102.5433 -0.2390864 1.43821920 -0.07056378 -0.08113996 NA
## Jul 2019 102.2975 -0.2396594 1.27410041 0.10911897 -0.65172979 NA
## Aug 2019 102.1092 -0.1840959 1.18332739 0.30397193 -1.31965625 NA
## Sep 2019 101.9209 -0.1844354 1.09238238 0.51417062 NA NA
## Oct 2019 101.6970 -0.2196659 0.79235201 0.68925526 NA NA
## Nov 2019 101.4731 -0.2201495 0.49278507 0.82905490 NA NA
## Dec 2019 101.1884 -0.2805220 -0.08113996 0.87506855 NA NA
## Jan 2020 100.9038 -0.2813111 -0.65172979 0.82708563 NA NA
## Feb 2020 100.5845 -0.3163907 -1.31965625 0.67686912 NA NA6.Grafico 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()