I have a linear regression model that is used to forecast the 'afluent natural energy' (ANE) of some region.
The predictors for this model are:
- the previous month ANE (
- the previous month rain volume (
- the current month forecast for rain volume (
We have 7 years of historical data for all of these variables, for each month. The current model just runs a OLS linear regression. I feel there's a lot of improvements to be done, but i'm not a time series specialist.
The first thing I notice is that the predictors are highly correlated (multicollinearity). I'm not certain of the impacts of multicollinearity on prediction confidence.
I decided to try a time series approach, so I ran a ACF and PACF on the historic data:
The ACF shows a sine wave pattern, and the PACF has a spike at 1 and 2. So I tried both
ARIMA (2, 0, 0) and
ARIMA(2,0,1) to predict 20 periods ahead.
The ARIMA(2,0,1) shows good results, but I'm not certain as to how to compare it to the linear regression model.
What's the best way to test the performance of these model? I'm using R as analysis tool (together with the