#Cargar librerias
library(lubridate)
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## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
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## date, intersect, setdiff, union
library(tseries)
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
library(lubridate)
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x lubridate::as.difftime() masks base::as.difftime()
## x lubridate::date() masks base::date()
## x dplyr::filter() masks stats::filter()
## x lubridate::intersect() masks base::intersect()
## x dplyr::lag() masks stats::lag()
## x lubridate::setdiff() masks base::setdiff()
## x lubridate::union() masks base::union()
library(car)
## Loading required package: carData
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## Attaching package: 'car'
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## recode
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## some
library(astsa)
library(foreign)
library(timsac)
library(vars)
## Loading required package: MASS
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## Attaching package: 'MASS'
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## select
## Loading required package: strucchange
## Loading required package: zoo
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## Attaching package: 'zoo'
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## as.Date, as.Date.numeric
## Loading required package: sandwich
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## Attaching package: 'strucchange'
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## boundary
## Loading required package: urca
## Loading required package: lmtest
library(lmtest)
library(mFilter)
library(dynlm)
library(nlme)
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## Attaching package: 'nlme'
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## collapse
library(lmtest)
library(broom)
library(kableExtra)
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## Attaching package: 'kableExtra'
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## group_rows
library(knitr)
library(MASS)
library(parallel)
library(car)
library(mlogit)
## Loading required package: dfidx
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## Attaching package: 'dfidx'
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## select
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## filter
library(dplyr)
library(tidyr)
library(forecast)
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## Attaching package: 'forecast'
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## getResponse
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library(fpp2)
## -- Attaching packages ---------------------------------------------- fpp2 2.4 --
## v fma 2.4 v expsmooth 2.3
## -- Conflicts ------------------------------------------------- fpp2_conflicts --
## x forecast::getResponse() masks nlme::getResponse()
## x car::some() masks purrr::some()
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## Attaching package: 'fpp2'
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## oil
library(stats)
library(quantmod)
## Loading required package: xts
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## Attaching package: 'xts'
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## first, last
## Loading required package: TTR
library(readxl)
Arimar <- read_excel("D:/Arimar.xlsx")
View(Arimar)
attach(Arimar)
names(Arimar)
## [1] "Precio"
#Series de Tiempo Univariadas
#Paso 1. Convertir a objeto de Serie de Tiempo en R
Arimar.ts=ts(Arimar, start=c(2013,1), frequency = 12)
print(Arimar.ts)
## Jan Feb Mar Apr May Jun Jul
## 2013 97.98278 103.35294 100.77090 97.75538 97.90686 96.99315 99.67255
## 2014 88.31081 90.82224 91.39522 93.88863 95.08631 97.01105 93.32017
## 2015 40.45127 45.98606 46.01733 49.32555 52.03910 52.82573 45.16595
## 2016 23.10238 23.72933 28.32656 31.33582 36.72636 39.80715 37.97153
## 2017 44.50008 44.17497 41.93243 43.22118 43.85074 41.15105 43.87771
## 2018 57.45399 56.15676 57.22458 58.15973 62.89929 64.63686 66.42162
## 2019 54.05666 57.37981 59.46449 62.07703 60.33674 56.90537 57.87580
## 2020 49.79299 44.63505 28.92302
## Aug Sep Oct Nov Dec
## 2013 99.24414 98.14572 93.87329 88.48590 89.40966
## 2014 89.50745 84.12824 72.50936 63.47980 50.18138
## 2015 38.41927 36.29036 34.86077 32.50883 26.19610
## 2016 37.74090 36.82666 40.66836 38.36162 42.32596
## 2017 45.55484 48.16237 48.89114 53.34505 54.05576
## 2018 64.26567 68.36348 71.15330 59.82583 51.86867
## 2019 49.58496 55.05522 51.00463 50.68969 54.54901
## 2020
#library(rio)
#export(Arimar.ts,"Arimar.ts.xlsx")
library(readxl)
df <- read_excel("Arimarlevene.xlsx")
df$mes=as.factor(df$mes)
#shapiro.test(df$Precio)
leveneTest(df$Precio~df$mes,data = df)
getwd()
## [1] "C:/Users/DELL/Desktop/para series de tiempo 2021"
library(readxl)
k <- read_excel("kruskall.xlsx")
head(k)
#View(k)
k$mes=as.factor(k$mes)
#Ho:La serie no posee componente estacional.
#H1:La series posee componente estacional.
kruskal.test(k$Precio~k$mes)
##
## Kruskal-Wallis rank sum test
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## data: k$Precio by k$mes
## Kruskal-Wallis chi-squared = 3.0289, df = 1, p-value = 0.08179
tapply(k$Precio, k$mes, median)
## 1 2
## 72.50936 52.60686
BoxCox.lambda(Arimar.ts)
## [1] 0.05546431
class(Arimar.ts)
## [1] "ts"
start(Arimar.ts)
## [1] 2013 1
end(Arimar.ts)
## [1] 2020 3
plot(Arimar.ts, main="Serie de tiempo", ylab="Precio", col="red")
library(TSstudio)
ts_plot(Arimar.ts)
tsdisplay(Arimar.ts)
ts_seasonal(Arimar.ts)
ggseasonplot(Arimar.ts, year.labels=TRUE, year.labels.left=TRUE) +
ylab("$ Precios") +
ggtitle("Seasonal plot")
ggsubseriesplot(Arimar.ts) +
ylab("$ Precios") +
ggtitle("Seasonal subseries plot")
serielog=log(Arimar.ts)
serielog
## Jan Feb Mar Apr May Jun Jul Aug
## 2013 4.584792 4.638150 4.612850 4.582468 4.584017 4.574640 4.601890 4.597583
## 2014 4.480862 4.508904 4.515193 4.542109 4.554785 4.574825 4.536036 4.494322
## 2015 3.700098 3.828338 3.829018 3.898442 3.951995 3.966998 3.810343 3.648559
## 2016 3.139936 3.166712 3.343800 3.444762 3.603495 3.684046 3.636837 3.630744
## 2017 3.795491 3.788158 3.736059 3.766331 3.780792 3.717249 3.781407 3.818917
## 2018 4.050985 4.028147 4.046984 4.063193 4.141535 4.168785 4.196023 4.163026
## 2019 3.990033 4.049692 4.085379 4.128376 4.099941 4.041390 4.058299 3.903688
## 2020 3.907874 3.798519 3.364638
## Sep Oct Nov Dec
## 2013 4.586453 4.541946 4.482843 4.493229
## 2014 4.432342 4.283716 4.150722 3.915644
## 2015 3.591552 3.551362 3.481512 3.265610
## 2016 3.606222 3.705450 3.647058 3.745401
## 2017 3.874578 3.889596 3.976781 3.990016
## 2018 4.224839 4.264837 4.091438 3.948715
## 2019 4.008337 3.931916 3.925723 3.999100
## 2020
plot(serielog)
#Estacionariedad: Para conocer el número de diferencias que se requieren para lograr que la serie
#sea estacionaria
library(forecast)
ndiffs(Arimar.ts)
## [1] 1
#Estacionariedad: Para conocer el número de diferencias que se requieren para lograr que la serie
#sea estacionaria
nsdiffs(Arimar.ts)
## [1] 0
#Paso 2.Prueba de DickeyFuller
adf.test(Arimar.ts)
##
## Augmented Dickey-Fuller Test
##
## data: Arimar.ts
## Dickey-Fuller = -1.6185, Lag order = 4, p-value = 0.7328
## alternative hypothesis: stationary
seriedif=diff(Arimar.ts)
plot(seriedif)
acf(seriedif)
library(fpp2)
ggAcf(Arimar.ts,lag=48)
library(TSstudio)
ndiffs(seriedif)
## [1] 0
adf.test(seriedif)
##
## Augmented Dickey-Fuller Test
##
## data: seriedif
## Dickey-Fuller = -3.3327, Lag order = 4, p-value = 0.07162
## alternative hypothesis: stationary
#Prueba de Dickey Fuller con dos diferencias
seriedif2=diff(Arimar.ts, differences =2)
plot(seriedif2)
adf.test(seriedif2)
## Warning in adf.test(seriedif2): p-value smaller than printed p-value
##
## Augmented Dickey-Fuller Test
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## data: seriedif2
## Dickey-Fuller = -5.1673, Lag order = 4, p-value = 0.01
## alternative hypothesis: stationary
plot(seriedif2, type="o", lty="dashed",main="Serie de Tiempo",col="red")
par(mfrow=c(2,1), mar=c(4,4,4,1)+.1)
acf(seriedif2)
pacf(seriedif2)
acf(ts(seriedif2, frequency=1))
pacf(ts(seriedif2, frequency=1))
#Modelo Arima
modelo1=arima(Arimar.ts,order=c(1,2,1))
summary(modelo1)
##
## Call:
## arima(x = Arimar.ts, order = c(1, 2, 1))
##
## Coefficients:
## ar1 ma1
## 0.4173 -1.000
## s.e. 0.1100 0.047
##
## sigma^2 estimated as 17.51: log likelihood = -244.07, aic = 494.14
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.1842755 4.136183 3.135096 -0.2061955 6.279221 0.9152235
## ACF1
## Training set -0.02472726
modelo1
##
## Call:
## arima(x = Arimar.ts, order = c(1, 2, 1))
##
## Coefficients:
## ar1 ma1
## 0.4173 -1.000
## s.e. 0.1100 0.047
##
## sigma^2 estimated as 17.51: log likelihood = -244.07, aic = 494.14
tsdiag(modelo1)
Box.test(residuals(modelo1),type="Ljung-Box")
##
## Box-Ljung test
##
## data: residuals(modelo1)
## X-squared = 0.055051, df = 1, p-value = 0.8145
error=residuals(modelo1)
plot(error)
#Pronosticos Arima
pronostico=forecast::forecast(modelo1,h=16)
pronostico
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Apr 2020 21.856654 16.4633719 27.24994 13.608341 30.10497
## May 2020 18.398192 8.9934192 27.80296 4.014833 32.78155
## Jun 2020 16.445341 3.6136910 29.27699 -3.178974 36.06966
## Jul 2020 15.120795 -0.6776994 30.91929 -9.040917 39.28251
## Aug 2020 14.058445 -4.3652157 32.48211 -14.118113 42.23500
## Sep 2020 13.105513 -7.6904599 33.90149 -18.699183 44.91021
## Oct 2020 12.198241 -10.7797404 35.17622 -22.943549 47.34003
## Nov 2020 11.310024 -13.7033148 36.32336 -26.944576 49.56462
## Dec 2020 10.429758 -16.5031190 37.36264 -30.760523 51.62004
## Jan 2021 9.552811 -19.2060240 38.31165 -34.430032 53.53565
## Feb 2021 8.677249 -21.8303878 39.18488 -37.980155 55.33465
## Mar 2021 7.802264 -24.3894412 39.99397 -41.430701 57.03523
## Apr 2021 6.927520 -26.8931307 40.74817 -44.796702 58.65174
## May 2021 6.052878 -29.3491818 41.45494 -48.089900 60.19566
## Jun 2021 5.178277 -31.7637487 42.12030 -51.319676 61.67623
## Jul 2021 4.303693 -34.1418321 42.74922 -54.493664 63.10105
plot(pronostico)
