March 2019
Beginner to intermediate
448 pages
13h 14m
English
Content preview from R Statistics Cookbook
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How to do it...
Temperature prediction is particularly important for electricity generation, because electricity demand is highly dependent on it. We will work with a monthly time series containing average temperatures for Buenos Aires, Argentina from January 2001 to December 2018. The objective is to use the forecast package to build a model automatically that can be used for prediction.
- We load the forecast package, and then we load the temperature dataset, and we transform the data into a proper time series:
library(forecast) average_temp = read.csv("./temperature.csv") raverage_temp$indice_tiempo = as.Date(average_temp$indice_tiempo,"%Y-%m-%d") average_temp = ts(average_temp$temperatura_promedio,start=c(2001,1),frequency = 12)
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