Running the Analysis

To use the Expert Modeler:

  1. From the menus choose:

    Analyze > Forecasting > Create Traditional Models...

    Figure 1. Time Series Modeler dialog box
    Time Series Modeler dialog box
  2. Select Subscribers for Market 1 through Subscribers for Market 85 for dependent variables.
  3. Verify that Expert Modeler is selected in the Method drop-down list. The Expert Modeler will automatically find the best-fitting model for each of the dependent variable series.

    The set of cases used to estimate the model is referred to as the estimation period. By default, it includes all of the cases in the active dataset. You can set the estimation period by selecting Based on time or case range in the Select Cases dialog box. For this example, we will stick with the default.

    Notice also that the default forecast period starts after the end of the estimation period and goes through to the last case in the active dataset. If you are forecasting beyond the last case, you will need to extend the forecast period. This is done from the Options tab as you will see later on in this example.

  4. Click Criteria.
    Figure 2. Expert Modeler Criteria dialog box, Model tab
    Expert Modeler Criteria dialog box, Model tab
  5. Deselect Expert Modeler considers seasonal models in the Model Type group.

    Although the data is monthly and the current periodicity is 12, we have seen that the data does not exhibit any seasonality, so there is no need to consider seasonal models. This reduces the space of models searched by the Expert Modeler and can significantly reduce computing time.

  6. Click Continue.
  7. Click the Options tab on the Time Series Modeler dialog box.
    Figure 3. Time Series Modeler, Options tab
    Time Series Modeler, Options tab
  8. Select First case after end of estimation period through a specified date in the Forecast Period group.
  9. In the Date grid, enter 2004 for the year and 3 for the month.

    The dataset contains data from January 1999 through December 2003. With the current settings, the forecast period will be January 2004 through March 2004.

  10. Click the Save tab.
    Figure 4. Time Series Modeler, Save tab
    Time Series Modeler, Save tab
  11. Select (check) the entry for Predicted Values in the Save column, and leave the default value Predicted as the Variable Name Prefix.

    The model predictions are saved as new variables in the active dataset, using the prefix Predicted for the variable names. You can also save the specifications for each of the models to an external XML file. This will allow you to reuse the models to extend your forecasts as new data becomes available.

  12. Click the Browse button on the Save tab.

    This will take you to a standard dialog box for saving a file.

  13. Navigate to the folder where you would like to save the XML model file, enter a filename, and click Save.
  14. Click the Statistics tab.
    Figure 5. Time Series Modeler, Statistics tab
    Time Series Modeler, Statistics tab
  15. Select Display forecasts.

    This option produces a table of forecasted values for each dependent variable series and provides another option--other than saving the predictions as new variables--for obtaining these values.

    The default selection of Goodness of fit (in the Statistics for Comparing Models group) produces a table with fit statistics—such as R-squared, mean absolute percentage error, and normalized BIC—calculated across all of the models. It provides a concise summary of how well the models fit the data.

  16. Click the Plots tab.
    Figure 6. Time Series Modeler, Plots tab
    Time Series Modeler, Plots tab
  17. Deselect Series in the Plots for Individual Models group.

    This suppresses the generation of series plots for each of the models. In this example, we are more interested in saving the forecasts as new variables than generating plots of the forecasts.

    The Plots for Comparing Models group provides several plots (in the form of histograms) of fit statistics calculated across all models.

  18. Select Mean absolute percentage error and Maximum absolute percentage error in the Plots for Comparing Models group.

    Absolute percentage error is a measure of how much a dependent series varies from its model-predicted level. By examining the mean and maximum across all models, you can get an indication of the uncertainty in your predictions. And looking at summary plots of percentage errors, rather than absolute errors, is advisable since the dependent series represent subscriber numbers for markets of varying sizes.

  19. Click OK in the Time Series Modeler dialog box.

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