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Lately I’ve been using Rob J Hyndman‘s excellent forecast package. The package comes with some built in plotting functions but I found I wanted to customize and make my own plots in ggplot. In order to do that, I need a generalizable function that will extract all the data I want (forecasts, fitted values, training data, actual observations in the forecast period, confidence intervals, et cetera) and place it into a data.frame with a properly formatted date field (ie, not a ts() object).
The function below does all that and should work for any forecast object (though I’ve only tested it on Arima() outputs). The only arguments it takes are the original observations and the forecast object (whatever results from calling forecast()). In my next post I’ll give some examples of plotting the results using ggplot and explain why I wanted more than the default plot.forecast() function.
#--Produces a data.frame with the Source Data+Training Data, Fitted Values+Forecast Values, forecast data Confidence Intervals
funggcast<-function(dn,fcast){
require(zoo) #needed for the 'as.yearmon()' function
en<-max(time(fcast$mean)) #extract the max date used in the forecast
#Extract Source and Training Data
ds<-as.data.frame(window(dn,end=en))
names(ds)<-'observed'
ds$date<-as.Date(time(window(dn,end=en)))
#Extract the Fitted Values (need to figure out how to grab confidence intervals)
dfit<-as.data.frame(fcast$fitted)
dfit$date<-as.Date(time(fcast$fitted))
names(dfit)[1]<-'fitted'
ds<-merge(ds,dfit,all.x=T) #Merge fitted values with source and training data
#Exract the Forecast values and confidence intervals
dfcastn<-as.data.frame(fcast)
dfcastn$date<-as.Date(as.yearmon(row.names(dfcastn)))
names(dfcastn)<-c('forecast','lo80','hi80','lo95','hi95','date')
pd<-merge(ds,dfcastn,all.x=T) #final data.frame for use in ggplot
return(pd)
}
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