If the model does not fit the data, examine the plot for trends or seasonality. If you see evidence of a trend or seasonality, you should use a different time series analysis. For more information, go to Which time series analysis should I use?.
Step 2: Compare the fit of your model to other models
Use the accuracy measures (MAPE, MAD, and MSD) to compare the fit of your model to other time series models. These statistics are not very informative by themselves, but you can use them to compare the fits obtained by using different methods. For all 3 statistics, smaller values usually indicate a better fitting model. If a single model does not have the lowest values for all 3 statistics, MAPE is usually the preferred measurement.
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.
Step 3: Determine whether the forecasts are accurate
Examine the fits and the forecasts in the plot to determine whether the forecasts are likely to be accurate. The forecasts should generally follow the data at the end of the series. If the fits shift away from the data at the end of the series, the forecasts may not be accurate. Because the forecasts from moving average 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 moving average 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.