March 2019
Beginner to intermediate
448 pages
13h 14m
English
Content preview from R Statistics Cookbook
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How to do it...
In this recipe, we will work with a time series containing data for automobiles produced in Brazil from January 1981 to December 2018. The objective will be to model this series and ultimately make predictions:
- We first load the dataset and we transform it into a time series object:
library(forecast) car_production = read.csv(".../car_production.csv")car_production$indice_tiempo = as.Date(car_production$indice_tiempo,"%Y-%m-%d") car_production = ts(car_production$produccion_automotriz_unidades,start=c(1981,1), frequency = 12)
- We then plot the series, as it can be seen it is non-stationary. A complex nonlinear trend could be fitted here, but we choose to remove the nonstationarity by differencing. The ACF shows two peaks ...
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