I modeled a univariate time series in R using the
Arima command. One can obtain fitted values for the original series using this command by applying the function
fitted to the model. However, I noticed that the fitted data has the same dimension as the original data. Hence, a fitted value for the first value in the time series was computed even though there is no past data. I checked wheter it is the mean of the series or the intercept of the model but that isn't the solution. What are possible approaches to get a fitted value here?
The code below is a reproducible example using the
Lynx data set.
> library(xts) > library(forecast) > > data("lynx") > > Y <- as.xts(log10(lynx)) > model <- Arima(Y, order= c(1, 0, 0)) #Fit AR(1) model > fit <- fitted(model) > length(fit) == length(Y)  TRUE