Per this post, you can force seasonality in auto.arima by selecting D=1.
I have a weekly time series which looks like it might (or might not) have a seasonal component (I have a priori reasons for thinking it might have a seasonal component).
Data <- as.ts(Data$Sales,order.by=Data$Date, frequency=52) Train <- window(Data,start=3,end=107) Test <- window(Data,start=108,end=116)
I tried manually fitting a seasonal model:
fit <- arima(Train, order=c(2,0,1) , seasonal = list (order= c(0,1,0) , period = 52)) forec <- predict(fit, n.ahead =8)
gave an “OK” forecast (see first graph).
So I tried improving on it by using auto.arima to find the best model.
AutoFit <- auto.arima(Train)
This returned an ARIMA(1,1,1) model, which I then fit using:
#fit <- arima(Train, order=c(1,1,1))
So I tried to force seasonality by running:
AutoFit <- auto.arima(Train, D=1)
But I still get the same ARIMA(1,1,1) model.
Why is auto.arima not trying to fit a seasonal model, even why I try to force it?
I’ve also tried:
AutoFit <- auto.arima(Train, seasonal=TRUE, D=1)
AutoFit <- auto.arima(Train, seasonal=TRUE, start.P=0, start.Q=0 , D=1)