# AIC - different values based on different R functions

I am a beginner in the whole forecasting/regression/time-series topic. While reading "Forecasting: principles and practice" from Rob J Hyndman and George Athana­sopou­los i found something strange.

library(fpp)
test <- tslm(ausbeer ~ trend+season)
#summary(test)
#plot(ausbeer, col='grey')
#lines(fitted(test), col='red')
AIC(test)
> 2327.316
CV(test)
CV          AIC         AICc          BIC        AdjR2
3582.0767318 1728.5234412 1728.9352059 1748.6345900    0.5377408


Why is there a difference in the AIC values (Akaike's Information Criterion)? As far as i understood AIC(test) and the AIC value in the CV(test) should be the same.

• It's very common for different packages/computations to include/exclude different constant terms in the AIC ... – Ben Bolker Sep 3 '15 at 16:10
• So in order to use AIC for comparing different models i have to take care that i use always the same AIC function!? – RandomDude Sep 4 '15 at 9:51
• the difference is between extractAIC() (used by CV()) which leaves out a constant term, and AIC(), which appears to give a computation consistent with the full Normal log-likelihood expression. – Ben Bolker Sep 5 '15 at 3:10
• – Ben Bolker Sep 5 '15 at 3:18