The accuracy measures provide an indication of the accuracy you might expect when you forecast out 1 period from the end of the data. Therefore, they do not indicate the accuracy of forecasting out more than 1 period. If you're using the model for forecasting, you shouldn't base your decision solely on accuracy measures. You should also examine the fit of the model to ensure that the forecasts and the model follow the data closely, especially at the end of the series.
Model 1
MAPE | 7.265 |
---|---|
MAD | 16.621 |
MSD | 518.119 |
Model 2
MAPE | 2.474 |
---|---|
MAD | 9.462 |
MSD | 135.701 |
In these results, all three numbers are lower for the 2nd model compared to the 1st model. Therefore, the 2nd model provides the better fit.
Examine the fits and the forecasts in the plot to determine whether the forecasts are likely to be accurate. The fits should follow the data closely, especially at the end of the series. When using a seasonal model it is especially important to verify that the fits match the actual values at the end of the time series. If the seasonal pattern or trend do not match up with the fits at the end of the data, the forecasts are likely to be less accurate. In this case, collect more data so the model can adapt to changes in the seasonal pattern or trend.
If the model fits the data at the end of the series, you can usually safely predict at least one full seasonal cycle.