Running scenarios

Given a temporal causal model system, you can run user-defined scenarios. A scenario is defined by a time series, that is referred to as the root series, and a set of user-defined values for that series over a specified time range. The specified values are then used to generate predictions for the time series that are affected by the root series. The analysis requires a temporal causal model system and the data that was used to build the system. In this example, the active dataset is the data that was used to build the model system.

To run scenarios:

1. From the menus, choose:

   Analyze > Forecasting > Apply Temporal Causal Models...

   

2. Browse to the location where you saved the temporal causal model system file and select the file.
3. Click the button that is labeled Run scenarios to investigate how specific values of inputs affect predictions.
4. Click Continue.
5. In the Temporal Causal Model Scenarios dialog, click Define Scenario Period.

   

6. Select Specify by time intervals relative to end of estimation period.
7. Enter -3 for the starting interval and enter 0 for the ending interval.

   These settings specify that each scenario is based on values that are specified for the last four time intervals in the estimation period. For this example, the last four time intervals means the last four weeks. The time range over which the scenario values are specified is referred to as the scenario period.

8. Enter 4 for the intervals to predict past the end of the scenarios values.

   This setting specifies that predictions are generated for four time intervals beyond the end of the scenario period.

9. Click Continue.
10. Click Add Scenario on the Scenarios tab.

    

11. Move Lever3 to the Root Field box to examine how specified values of Lever3 in the scenario period affect predictions of the other series that are causally affected by Lever3.
12. Enter Lever3_25pct for the scenario ID.
13. Select Specify expression for scenario values for root field and enter Lever3*1.25 for the expression.

    This setting specifies that the values for Lever3 in the scenario period are 25% larger than the observed values. For more complex expressions, you can use the Expression Builder by clicking the calculator icon.

14. Click Continue.
15. Repeat steps 10 - 14 to define a scenario that has Lever3 for the root field, Lever3_50pct for the scenario ID, and Lever3*1.5 for the expression.

    

16. Click the Options tab and enter 2 for the maximum level for affected targets.
17. Click Run.
18. Double-click the Impact Diagram chart for Lever3_50pct in the Viewer to activate it.

    

    The Impact Diagram shows the series that are affected by the root series Lever3. Two levels of effects are shown because you specified 2 for the maximum level for affected targets.

    The Forecasted Values table includes the predictions for all of the series that are affected by Lever3, up to the second level of effects. Predictions for target series in the first level of effects start at the first time period after the beginning of the scenario period. In this example, predictions for target series in the first level start at 2010-10-10. Predictions for target series in the second level of effects start at the second time period after the beginning of the scenario period. In this example, predictions for target series in the second level start at 2010-10-17. The staggered nature of the predictions reflects the fact that the time series models are based on lagged values of the inputs.

19. Click the node for KPI_5 to generate a detailed sequence diagram.

    

    The sequence chart shows the predicted values from the scenario, and it also shows the values of the series in the absence of the scenario. When the scenario period contains times within the estimation period, the observed values of the series are shown. For times beyond the end of the estimation period, the original forecasts are shown.

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