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 |
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MAD | 16.621 |
MSD | 518.119 |
Model 2
MAPE | 2.474 |
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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. If the fits shift away from the data at the end of the series, or if the trend line in the forecasts does match the general flow of the data, the underlying trend may still be adjusting to the change. Try collecting more data to determine whether any changes in the underlying trend are short-term or appear to be long-term.
Even if your forecasts appear to be accurate, you should be cautious in projecting forecasts too far into the future. You should usually only forecast 6 periods into the future.