I am doing some time series forecasting task up to 4-week ahead (in weekly scale).
However, I think that even if I fit a ARIMA model (with some optimisation on selecting parameters) to the time series, the RMSE is worse than a simple baseline model
In the baseline model, I just use the last observation t, as a predictor of t+1 to t+4.
I wonder what is wrong on my ARIMA model? is there any thing I missed to do for the time series input before forecasting?
Edit: some details are added:
- the frequency is weekly. And there are about 15 year of data
- I am talking about the within-sample prediction, not out-of-sample prediction