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 |
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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.
Decomposition uses a fixed trend line and fixed seasonal indices. Because both the trend and the seasonal indices are fixed, you should only use decomposition to forecast when the trend and seasonality are very consistent. 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, use Winters' Method.