I'm trying to fit an ARIMA model in R, but
auto.arima and the standard
arima function for some reason keep giving me different results and different forecasts.
For example, if I fit the model with
arima, I use:
ARIMA011 <- arima(data, order=c(0,0,1))
and it gives me:
Call: arima(x = data, order = c(0, 1, 1)) Coefficients: ma1 0.6096 s.e. 0.0403 sigma^2 estimated as 0.0381: log likelihood = 64.42, aic = -124.85
If I use
Series: data ARIMA(0,1,1) with drift Coefficients: ma1 drift 0.3691 0.1908 s.e. 0.0578 0.0118 sigma^2 estimated as 0.02249: log likelihood=145.03 AIC=-284.06 AICc=-283.98 BIC=-272.94
Even the AIC for the models are different. Can someone explain why this is the case, and which of the methods is preferable?