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 forecasts should follow the general flow of the data at the end of the series. Because the forecasts from single exponential smoothing are constant, it is important that there is no trend in the data before the forecasts. If there is a trend before the forecasts, the forecasts may not be accurate.
The forecasts from single exponential smoothing are very conservative because they are based solely on the latest estimate of the level, and no estimate of the trend. You should usually only forecast 6 periods into the future.