Práctica 4 - Series de Tiempo
Rojas Zepeda Rodrigo
Funciones
Función para sacar la TENDENCIA de una Serie de Tiempo
tendencia<-function(serie,frec) {
if(class(serie)=="ts"){
s<-as.numeric(serie)
mean_number<-ma(serie, frec, centre=TRUE)
periodo<-na.omit(mean_number); mean_number<-as.numeric(mean_number)
if(periodo[1]<periodo[length(periodo)]){
tendencia <-"Tendencia Creciente"
} else if(periodo[1]>periodo[length(periodo)]){
tendencia <- "Tendencia Decreciente"
}else{
tendencia<-"Tendencia Constante"
}
Tiempo<-c(1:length(serie))
Tiempo1<-c(1:length(mean_number))
g1<-ggplot()+geom_line(aes(x=Tiempo, y=mean_number),color="orange")+labs(x="Tiempo", y="Tendencia")+
ggtitle("Tendencia ocupando Medias Moviles",subtitle = tendencia)+theme_calc()
return(list(g1,mean_number))
}else{
stop('class(serie) no es ts')
}
}Ejemplos
Ejemplo 1
serie1<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s1<-ts(series[687,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie1[1])
tendencia(s1,10)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA 394.60 397.95 402.25 403.75 403.25
## [11] 406.30 418.25 428.10 423.70 415.75 414.85 414.65 415.00 416.50 418.70
## [21] 420.00 418.00 424.15 440.15 452.55 447.90 440.70 441.50 441.40 440.80
## [31] 441.85 441.10 435.50 430.85 431.65 441.10 448.70 440.80 430.95 426.65
## [41] 422.15 423.35 425.10 422.40 418.20 415.60 416.85 425.10 431.70 423.10
## [51] 415.25 414.30 413.55 414.65 414.65 414.90 415.70 414.65 416.50 428.25
## [61] 437.05 429.00 420.10 416.80 415.60 414.15 411.20 412.15 412.95 412.00
## [71] 412.75 423.30 433.00 426.60 419.30 418.70 418.40 416.55 414.00 NA
## [81] NA NA NA NAEjemplo 2
serie2<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s2<-ts(series[1001,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie2[1])
tendencia(s2,10)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA 39.95 41.75 44.15 44.20 44.80 45.55
## [12] 44.60 42.90 41.10 41.15 42.70 41.65 39.00 39.05 40.30 39.60 38.35
## [23] 37.65 36.20 33.95 32.40 32.40 33.85 34.35 33.10 33.20 34.60 34.10
## [34] 33.50 34.00 33.60 32.35 31.80 32.15 33.60 34.40 33.10 33.90 36.25
## [45] 35.85 34.75 34.90 33.90 32.50 32.20 33.65 35.90 36.15 34.95 35.25
## [56] NA NA NA NA NAEjemplo 3
serie3<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s3<-ts(series[849,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie3[1])
tendencia(s3,10)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA 119.115 122.355 125.810
## [9] 129.825 134.555 139.845 145.825 151.500 156.285 160.500 164.090
## [17] 166.605 168.170 168.520 167.495 165.460 162.160 158.395 155.075
## [25] 151.900 148.610 145.340 142.530 140.355 138.700 137.175 136.045
## [33] 135.510 135.135 135.095 135.710 137.025 138.665 140.395 142.540
## [41] 145.320 148.295 151.100 153.830 156.390 158.765 161.035 163.110
## [49] 164.955 166.275 166.945 166.720 166.395 166.545 166.545 166.370
## [57] 166.005 165.370 NA NA NA NA NAFunción para OBTENER LA ESTACIONALIDAD de una Serie
estacionalidad <- function(serie, tipo, frec, medias_moviles){
if(class(serie)=="ts"){
if(tipo=="aditiva"){
tend<-tendencia(serie,medias_moviles); s<-as.numeric(serie)
detrend<-s-tend[[2]]
m<-t(matrix(data = detrend, nrow = 4))
estac<-colMeans(m, na.rm=TRUE)
estac<-rep_len(estac, length.out = length(s))
df<-data.frame(Tiempo=c(1:length(s)), Serie=s, Tendencia=tend[[2]], Estacionalidad=estac)
g1<-ggplot(df)+geom_line(aes(x=Tiempo, y=Estacionalidad),
color="darkblue", size=1)+geom_point(aes(x=Tiempo, y=Estacionalidad), color="orange")+labs(x="Tiempo", y="Estacionalidad")+ ggtitle("Estacionalidad de Serie de Tiempo")+theme_calc()
return(list(g1, df$Estacionalidad))
}else if(tipo=="multiplicativa"){
tend<-tendencia(serie,medias_moviles); s<-as.numeric(serie)
detrend<-s/tend[[2]]
m<-t(matrix(data = detrend, nrow = 4))
estac<-colMeans(m, na.rm=TRUE)
estac<-rep_len(estac, length.out = length(s))
df<-data.frame(Tiempo=c(1:length(s)), Serie=s, Tendencia=tend[[2]], Estacionalidad=estac)
g1<-ggplot(df)+geom_line(aes(x=Tiempo, y=Estacionalidad), color="orange", size=1.1)+geom_point(aes(x=Tiempo, y=Estacionalidad), color="darkblue")+labs(x="Tiempo", y="Estacionalidad")+ ggtitle( "Estacionalidad de Serie de Tiempo")+theme_calc()
return(list(g1, df$Estacionalidad))
}else {stop('El parametro es incorrecto')}
}else{stop('class(serie) no es ts')}
}Ejemplos
Ejemplo 1
serie1<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s1<-ts(series[687,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie1[1])
estacionalidad(s1,"multiplicativa",10,10)## [[1]]
##
## [[2]]
## [1] 1.0520995 0.9951265 0.9257109 1.0112225 1.0520995 0.9951265 0.9257109
## [8] 1.0112225 1.0520995 0.9951265 0.9257109 1.0112225 1.0520995 0.9951265
## [15] 0.9257109 1.0112225 1.0520995 0.9951265 0.9257109 1.0112225 1.0520995
## [22] 0.9951265 0.9257109 1.0112225 1.0520995 0.9951265 0.9257109 1.0112225
## [29] 1.0520995 0.9951265 0.9257109 1.0112225 1.0520995 0.9951265 0.9257109
## [36] 1.0112225 1.0520995 0.9951265 0.9257109 1.0112225 1.0520995 0.9951265
## [43] 0.9257109 1.0112225 1.0520995 0.9951265 0.9257109 1.0112225 1.0520995
## [50] 0.9951265 0.9257109 1.0112225 1.0520995 0.9951265 0.9257109 1.0112225
## [57] 1.0520995 0.9951265 0.9257109 1.0112225 1.0520995 0.9951265 0.9257109
## [64] 1.0112225 1.0520995 0.9951265 0.9257109 1.0112225 1.0520995 0.9951265
## [71] 0.9257109 1.0112225 1.0520995 0.9951265 0.9257109 1.0112225 1.0520995
## [78] 0.9951265 0.9257109 1.0112225 1.0520995 0.9951265 0.9257109 1.0112225Ejemplo 2
serie2<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s2<-ts(series[1001,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie2[1])
estacionalidad(s2,"aditiva",10,10)## [[1]]
##
## [[2]]
## [1] 0.1666667 -3.9230769 -3.9692308 9.9458333 0.1666667 -3.9230769
## [7] -3.9692308 9.9458333 0.1666667 -3.9230769 -3.9692308 9.9458333
## [13] 0.1666667 -3.9230769 -3.9692308 9.9458333 0.1666667 -3.9230769
## [19] -3.9692308 9.9458333 0.1666667 -3.9230769 -3.9692308 9.9458333
## [25] 0.1666667 -3.9230769 -3.9692308 9.9458333 0.1666667 -3.9230769
## [31] -3.9692308 9.9458333 0.1666667 -3.9230769 -3.9692308 9.9458333
## [37] 0.1666667 -3.9230769 -3.9692308 9.9458333 0.1666667 -3.9230769
## [43] -3.9692308 9.9458333 0.1666667 -3.9230769 -3.9692308 9.9458333
## [49] 0.1666667 -3.9230769 -3.9692308 9.9458333 0.1666667 -3.9230769
## [55] -3.9692308 9.9458333 0.1666667 -3.9230769 -3.9692308 9.9458333Ejemplo 3
serie3<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s3<-ts(series[849,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie3[1])
estacionalidad(s3,"multiplicativa",10,10)## [[1]]
##
## [[2]]
## [1] 0.9997342 1.0025243 0.9971112 0.9982939 0.9997342 1.0025243 0.9971112
## [8] 0.9982939 0.9997342 1.0025243 0.9971112 0.9982939 0.9997342 1.0025243
## [15] 0.9971112 0.9982939 0.9997342 1.0025243 0.9971112 0.9982939 0.9997342
## [22] 1.0025243 0.9971112 0.9982939 0.9997342 1.0025243 0.9971112 0.9982939
## [29] 0.9997342 1.0025243 0.9971112 0.9982939 0.9997342 1.0025243 0.9971112
## [36] 0.9982939 0.9997342 1.0025243 0.9971112 0.9982939 0.9997342 1.0025243
## [43] 0.9971112 0.9982939 0.9997342 1.0025243 0.9971112 0.9982939 0.9997342
## [50] 1.0025243 0.9971112 0.9982939 0.9997342 1.0025243 0.9971112 0.9982939
## [57] 0.9997342 1.0025243 0.9971112 0.9982939 0.9997342 1.0025243 0.9971112Función para OBTENER ELEMENTO ALEATORIO de una Serie
aleatoriedad<-function(serie, tipo, frec, medias_moviles){
if(class(serie)=="ts"){
if(tipo=="aditiva"){
tend<-tendencia(serie,medias_moviles)
est <- estacionalidad(serie, tipo, frec, medias_moviles)
s<-as.numeric(serie)
error<-s-tend[[2]]-est[[2]]
df<-data.frame(Tiempo=c(1:length(s)), Error=error)
g1<-ggplot(df)+geom_line(aes(x=Tiempo, y=Error), color="orange", size=1.1)+geom_point(aes(x=Tiempo, y=Error), color="darkblue")+ labs(x="Tiempo", y="Error Aleatorio")+ ggtitle( "Error Aleatorio de Serie de Tiempo")+theme_calc()
return(list(g1,error))
}else if(tipo=="multiplicativa"){
tend<-tendencia(serie,medias_moviles)
est <- estacionalidad(serie, tipo, frec, medias_moviles)
s<-as.numeric(serie)
error<-s/(tend[[2]]*est[[2]])
df<-data.frame(Tiempo=c(1:length(s)), Error=as.numeric(error))
g1<-ggplot(df)+geom_line(aes(x=Tiempo, y=Error), color="orange", size=1)+geom_point(aes(x=Tiempo, y=Error), color="darkblue")+ labs(x="Tiempo", y="Error Aleatorio", title = "Error Aleatorio de Serie de Tiempo")+theme_calc()
return(list(g1,error))
}else(stop('El parametro es incorrecto'))
}else{stop('class(serie) no es ts')}
}Ejemplos
Ejemplo 1
serie1<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s1<-ts(series[687,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie1[1])
aleatoriedad(s1,"multiplicativa",10,10)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA 0.9498903 0.7926454
## [8] 0.8579908 1.0099222 1.0416543 1.1565570 1.0143191 0.9613595 1.0482998
## [15] 1.1120803 1.0560048 1.0567261 0.9128827 0.8118092 0.8856897 1.0410024
## [22] 0.9616243 1.1944775 0.9795781 1.0039325 0.9400580 1.0907911 1.1557723
## [29] 1.0637456 1.0076330 0.7334508 0.9326304 1.0235943 1.0122443 1.1712207
## [36] 1.0088549 0.8452010 1.0258707 1.1706164 1.1241474 1.0041792 1.0016929
## [43] 0.7750565 0.9388014 0.9432075 0.9986107 1.1920724 0.9863431 0.9665529
## [50] 1.0189103 1.1706511 1.0574067 1.0319567 0.9427350 0.6643289 0.9915239
## [57] 1.0014684 1.0445213 1.1775123 0.9559964 1.0221389 1.0283215 1.1108507
## [64] 1.0510643 0.9971354 0.9972542 0.7408335 0.9813440 0.9436904 1.0585569
## [71] 1.1018428 1.0022182 1.0119433 1.0129064 1.1078175 1.0203145 1.0722437
## [78] 0.9963332 0.7253858 NA NA NA NA NAEjemplo 2
serie2<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s2<-ts(series[1001,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie2[1])
aleatoriedad(s2,"aditiva",10,10)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA
## [6] 1.9730769 14.2192308 3.9041667 17.6333333 -1.8769231
## [11] -3.5807692 16.4541667 -1.0666667 -12.1769231 -6.1807692
## [16] -15.6458333 -7.8166667 6.9230769 6.9192308 9.7541667
## [21] 8.2333333 5.5730769 -13.6807692 1.8541667 -1.1166667
## [26] -7.4769231 -3.4307692 -6.7958333 -16.5166667 9.8230769
## [31] 8.7692308 5.4541667 4.7333333 9.4230769 -13.0307692
## [36] -4.5458333 2.4833333 -10.8769231 -2.1807692 -2.5458333
## [41] -6.5666667 5.8230769 13.0692308 -4.1958333 6.9833333
## [46] -1.8269231 -11.9307692 5.1541667 8.3333333 -10.2769231
## [51] -0.6807692 -8.8458333 -15.3166667 4.9730769 11.7192308
## [56] NA NA NA NA NAEjemplo 3
serie3<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s3<-ts(series[849,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie3[1])
aleatoriedad(s3,"multiplicativa",10,10)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA 0.9705593 0.9762171
## [8] 0.9888901 0.9838933 0.9622323 0.9674341 0.9926073 0.9698947 1.0307665
## [15] 1.0316406 1.0597641 1.0866908 1.0427386 1.0224171 1.0166902 1.0065530
## [22] 1.0038793 1.0098936 1.0070381 0.9890713 0.9920453 0.9715695 0.9888477
## [29] 1.0012992 0.9910096 0.9716423 0.9675075 0.9972394 1.0097720 1.0111003
## [36] 0.9691577 0.9832936 0.9790308 0.9829313 0.9831562 0.9849852 0.9921346
## [43] 1.0075432 1.0073743 1.0310305 1.0171785 1.0188715 1.0028759 1.0005387
## [50] 0.9820346 1.0020261 1.0166097 1.0315552 1.0289556 1.0267133 0.9994813
## [57] 0.9339550 0.9976630 NA NA NA NA NAFunción para DESCOMPONER la Serie
descomp <- function(serie, tipo, frec, medias_moviles){
if(class(serie)=="ts"){
if(tipo=="aditiva" | tipo=="multiplicativa"){
tend<-tendencia(serie,medias_moviles)
est<-estacionalidad(serie, tipo, frec, medias_moviles)
error <- aleatoriedad(serie, tipo, frec, medias_moviles)
df<-data.frame(Tiempo=c(1:length(serie)), Serie=as.numeric(serie), Tendencia=tend[[2]], Estacionalidad=est[[2]], Comp_Aleatorio=error[[2]])
g1<-ggplot(df)+geom_line(aes(x=Tiempo, y=Serie), color="orange", size=1.2)+geom_point(aes(x=Tiempo, y=Serie), color="darkblue")+labs(x="Tiempo", y="Observaciones", title = "Serie de Tiempo")+ theme_calc()
plots<-grid.arrange(g1, tend[[1]], est[[1]], error[[1]], ncol=2)
return(list(df,plots))
} else{stop('El parámetro no es correcto')}
}else{stop('class(serie) no es una ts')}
}Ejemplos
Ejemplo 1
serie1<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s1<-ts(series[687,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie1[1])
descomp(s1,"multiplicativa",10,10)
## [[1]]
## Tiempo Serie Tendencia Estacionalidad Comp_Aleatorio
## 1 1 423 NA 1.0520995 NA
## 2 2 374 NA 0.9951265 NA
## 3 3 402 NA 0.9257109 NA
## 4 4 443 NA 1.0112225 NA
## 5 5 437 NA 1.0520995 NA
## 6 6 373 394.60 0.9951265 0.9498903
## 7 7 292 397.95 0.9257109 0.7926454
## 8 8 349 402.25 1.0112225 0.8579908
## 9 9 429 403.75 1.0520995 1.0099222
## 10 10 418 403.25 0.9951265 1.0416543
## 11 11 435 406.30 0.9257109 1.1565570
## 12 12 429 418.25 1.0112225 1.0143191
## 13 13 433 428.10 1.0520995 0.9613595
## 14 14 442 423.70 0.9951265 1.0482998
## 15 15 428 415.75 0.9257109 1.1120803
## 16 16 443 414.85 1.0112225 1.0560048
## 17 17 461 414.65 1.0520995 1.0567261
## 18 18 377 415.00 0.9951265 0.9128827
## 19 19 313 416.50 0.9257109 0.8118092
## 20 20 375 418.70 1.0112225 0.8856897
## 21 21 460 420.00 1.0520995 1.0410024
## 22 22 400 418.00 0.9951265 0.9616243
## 23 23 469 424.15 0.9257109 1.1944775
## 24 24 436 440.15 1.0112225 0.9795781
## 25 25 478 452.55 1.0520995 1.0039325
## 26 26 419 447.90 0.9951265 0.9400580
## 27 27 445 440.70 0.9257109 1.0907911
## 28 28 516 441.50 1.0112225 1.1557723
## 29 29 494 441.40 1.0520995 1.0637456
## 30 30 442 440.80 0.9951265 1.0076330
## 31 31 300 441.85 0.9257109 0.7334508
## 32 32 416 441.10 1.0112225 0.9326304
## 33 33 469 435.50 1.0520995 1.0235943
## 34 34 434 430.85 0.9951265 1.0122443
## 35 35 468 431.65 0.9257109 1.1712207
## 36 36 450 441.10 1.0112225 1.0088549
## 37 37 399 448.70 1.0520995 0.8452010
## 38 38 450 440.80 0.9951265 1.0258707
## 39 39 467 430.95 0.9257109 1.1706164
## 40 40 485 426.65 1.0112225 1.1241474
## 41 41 446 422.15 1.0520995 1.0041792
## 42 42 422 423.35 0.9951265 1.0016929
## 43 43 305 425.10 0.9257109 0.7750565
## 44 44 401 422.40 1.0112225 0.9388014
## 45 45 415 418.20 1.0520995 0.9432075
## 46 46 413 415.60 0.9951265 0.9986107
## 47 47 460 416.85 0.9257109 1.1920724
## 48 48 424 425.10 1.0112225 0.9863431
## 49 49 439 431.70 1.0520995 0.9665529
## 50 50 429 423.10 0.9951265 1.0189103
## 51 51 450 415.25 0.9257109 1.1706511
## 52 52 443 414.30 1.0112225 1.0574067
## 53 53 449 413.55 1.0520995 1.0319567
## 54 54 389 414.65 0.9951265 0.9427350
## 55 55 255 414.65 0.9257109 0.6643289
## 56 56 416 414.90 1.0112225 0.9915239
## 57 57 438 415.70 1.0520995 1.0014684
## 58 58 431 414.65 0.9951265 1.0445213
## 59 59 454 416.50 0.9257109 1.1775123
## 60 60 414 428.25 1.0112225 0.9559964
## 61 61 470 437.05 1.0520995 1.0221389
## 62 62 439 429.00 0.9951265 1.0283215
## 63 63 432 420.10 0.9257109 1.1108507
## 64 64 443 416.80 1.0112225 1.0510643
## 65 65 436 415.60 1.0520995 0.9971354
## 66 66 411 414.15 0.9951265 0.9972542
## 67 67 282 411.20 0.9257109 0.7408335
## 68 68 409 412.15 1.0112225 0.9813440
## 69 69 410 412.95 1.0520995 0.9436904
## 70 70 434 412.00 0.9951265 1.0585569
## 71 71 421 412.75 0.9257109 1.1018428
## 72 72 429 423.30 1.0112225 1.0022182
## 73 73 461 433.00 1.0520995 1.0119433
## 74 74 430 426.60 0.9951265 1.0129064
## 75 75 430 419.30 0.9257109 1.1078175
## 76 76 432 418.70 1.0112225 1.0203145
## 77 77 472 418.40 1.0520995 1.0722437
## 78 78 413 416.55 0.9951265 0.9963332
## 79 79 278 414.00 0.9257109 0.7253858
## 80 80 420 NA 1.0112225 NA
## 81 81 423 NA 1.0520995 NA
## 82 82 421 NA 0.9951265 NA
## 83 83 432 NA 0.9257109 NA
## 84 84 408 NA 1.0112225 NA
##
## [[2]]
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]Ejemplo 2
serie2<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s2<-ts(series[1001,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie2[1])
descomp(s2,"multiplicativa",10,10)
## [[1]]
## Tiempo Serie Tendencia Estacionalidad Comp_Aleatorio
## 1 1 45 NA 0.9989731 NA
## 2 2 28 NA 0.8889304 NA
## 3 3 37 NA 0.8891741 NA
## 4 4 29 NA 1.2617274 NA
## 5 5 15 NA 0.9989731 NA
## 6 6 38 39.95 0.8889304 1.0700377
## 7 7 52 41.75 0.8891741 1.4007481
## 8 8 58 44.15 1.2617274 1.0411943
## 9 9 62 44.20 0.9989731 1.4041569
## 10 10 39 44.80 0.8889304 0.9793070
## 11 11 38 45.55 0.8891741 0.9382280
## 12 12 71 44.60 1.2617274 1.2617054
## 13 13 42 42.90 0.9989731 0.9800274
## 14 14 25 41.10 0.8889304 0.6842747
## 15 15 31 41.15 0.8891741 0.8472372
## 16 16 37 42.70 1.2617274 0.6867653
## 17 17 34 41.65 0.9989731 0.8171657
## 18 18 42 39.00 0.8889304 1.2114820
## 19 19 42 39.05 0.8891741 1.2095990
## 20 20 60 40.30 1.2617274 1.1799964
## 21 21 48 39.60 0.9989731 1.2133672
## 22 22 40 38.35 0.8889304 1.1733481
## 23 23 20 37.65 0.8891741 0.5974179
## 24 24 48 36.20 1.2617274 1.0509139
## 25 25 33 33.95 0.9989731 0.9730169
## 26 26 21 32.40 0.8889304 0.7291327
## 27 27 25 32.40 0.8891741 0.8677771
## 28 28 37 33.85 1.2617274 0.8663184
## 29 29 18 34.35 0.9989731 0.5245561
## 30 30 39 33.10 0.8889304 1.3254669
## 31 31 38 33.20 0.8891741 1.2872375
## 32 32 50 34.60 1.2617274 1.1453241
## 33 33 39 34.10 0.9989731 1.1448707
## 34 34 39 33.50 0.8889304 1.3096404
## 35 35 17 34.00 0.8891741 0.5623196
## 36 36 39 33.60 1.2617274 0.9199406
## 37 37 35 32.35 0.9989731 1.0830287
## 38 38 17 31.80 0.8889304 0.6013870
## 39 39 26 32.15 0.8891741 0.9095060
## 40 40 41 33.60 1.2617274 0.9671171
## 41 41 28 34.40 0.9989731 0.8147902
## 42 42 35 33.10 0.8889304 1.1895216
## 43 43 43 33.90 0.8891741 1.4265334
## 44 44 42 36.25 1.2617274 0.9182813
## 45 45 43 35.85 0.9989731 1.2006751
## 46 46 29 34.75 0.8889304 0.9388051
## 47 47 19 34.90 0.8891741 0.6122677
## 48 48 49 33.90 1.2617274 1.1455943
## 49 49 41 32.50 0.9989731 1.2628353
## 50 50 18 32.20 0.8889304 0.6288527
## 51 51 29 33.65 0.8891741 0.9692284
## 52 52 37 35.90 1.2617274 0.8168489
## 53 53 21 36.15 0.9989731 0.5815100
## 54 54 36 34.95 0.8889304 1.1587442
## 55 55 43 35.25 0.8891741 1.3719002
## 56 56 58 NA 1.2617274 NA
## 57 57 35 NA 0.9989731 NA
## 58 58 38 NA 0.8889304 NA
## 59 59 28 NA 0.8891741 NA
## 60 60 37 NA 1.2617274 NA
##
## [[2]]
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]Ejemplo 3
serie3<-c(as.numeric(fecha[123], "%y"),as.numeric(fecha[123], "%m"))
s3<-ts(series[849,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=serie3[1])
descomp(s3,"aditiva",10,10)
## [[1]]
## Tiempo Serie Tendencia Estacionalidad Comp_Aleatorio
## 1 1 105.4 NA 0.09653846 NA
## 2 2 109.2 NA 0.59678571 NA
## 3 3 113.1 NA -0.20346154 NA
## 4 4 115.0 NA -0.03461538 NA
## 5 5 117.0 NA 0.09653846 NA
## 6 6 115.9 119.115 0.59678571 -3.81178571
## 7 7 119.1 122.355 -0.20346154 -3.05153846
## 8 8 124.2 125.810 -0.03461538 -1.57538462
## 9 9 127.7 129.825 0.09653846 -2.22153846
## 10 10 129.8 134.555 0.59678571 -5.35178571
## 11 11 134.9 139.845 -0.20346154 -4.74153846
## 12 12 144.5 145.825 -0.03461538 -1.29038462
## 13 13 146.9 151.500 0.09653846 -4.69653846
## 14 14 161.5 156.285 0.59678571 4.61821429
## 15 15 165.1 160.500 -0.20346154 4.80346154
## 16 16 173.6 164.090 -0.03461538 9.54461538
## 17 17 181.0 166.605 0.09653846 14.29846154
## 18 18 175.8 168.170 0.59678571 7.03321429
## 19 19 171.8 168.520 -0.20346154 3.48346154
## 20 20 170.0 167.495 -0.03461538 2.53961538
## 21 21 166.5 165.460 0.09653846 0.94346154
## 22 22 163.2 162.160 0.59678571 0.44321429
## 23 23 159.5 158.395 -0.20346154 1.30846154
## 24 24 155.9 155.075 -0.03461538 0.85961538
## 25 25 150.2 151.900 0.09653846 -1.79653846
## 26 26 147.8 148.610 0.59678571 -1.40678571
## 27 27 140.8 145.340 -0.20346154 -4.33653846
## 28 28 140.7 142.530 -0.03461538 -1.79538462
## 29 29 140.5 140.355 0.09653846 0.04846154
## 30 30 137.8 138.700 0.59678571 -1.49678571
## 31 31 132.9 137.175 -0.20346154 -4.07153846
## 32 32 131.4 136.045 -0.03461538 -4.61038462
## 33 33 135.1 135.510 0.09653846 -0.50653846
## 34 34 136.8 135.135 0.59678571 1.06821429
## 35 35 136.2 135.095 -0.20346154 1.30846154
## 36 36 131.3 135.710 -0.03461538 -4.37538462
## 37 37 134.7 137.025 0.09653846 -2.42153846
## 38 38 136.1 138.665 0.59678571 -3.16178571
## 39 39 137.6 140.395 -0.20346154 -2.59153846
## 40 40 139.9 142.540 -0.03461538 -2.60538462
## 41 41 143.1 145.320 0.09653846 -2.31653846
## 42 42 147.5 148.295 0.59678571 -1.39178571
## 43 43 151.8 151.100 -0.20346154 0.90346154
## 44 44 154.7 153.830 -0.03461538 0.90461538
## 45 45 161.2 156.390 0.09653846 4.71346154
## 46 46 161.9 158.765 0.59678571 2.53821429
## 47 47 163.6 161.035 -0.20346154 2.76846154
## 48 48 163.3 163.110 -0.03461538 0.22461538
## 49 49 165.0 164.955 0.09653846 -0.05153846
## 50 50 163.7 166.275 0.59678571 -3.17178571
## 51 51 166.8 166.945 -0.20346154 0.05846154
## 52 52 169.2 166.720 -0.03461538 2.51461538
## 53 53 171.6 166.395 0.09653846 5.10846154
## 54 54 171.8 166.545 0.59678571 4.65821429
## 55 55 170.5 166.545 -0.20346154 4.15846154
## 56 56 166.0 166.370 -0.03461538 -0.33538462
## 57 57 155.0 166.005 0.09653846 -11.10153846
## 58 58 165.4 165.370 0.59678571 -0.56678571
## 59 59 165.9 NA -0.20346154 NA
## 60 60 162.8 NA -0.03461538 NA
## 61 61 164.2 NA 0.09653846 NA
## 62 62 164.5 NA 0.59678571 NA
## 63 63 163.6 NA -0.20346154 NA
##
## [[2]]
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]