# Scenario Group Definition (Temporal Causal Model Scenarios)

- Root Series
- Specifies the set of root series for the scenario group. An individual scenario is
generated for each time series in the set. You specify the root series by adding an entry
to the grid for the metric field that contains the series that you want. You then specify
the values of the dimension fields that define the set. By default, all series that are
contained in the specified root metric field are included. You can customize the set of
included series by customizing the included values for one or more of the dimension
fields. To customize the dimension values that are included, click the ellipsis button for
a dimension. This action opens the Select Dimension Values dialog.
The Series Count column displays the number of sets of dimension values that are currently included for the associated root metric. The displayed value can be larger than the actual number of root series for the scenario group (one series per set). This condition occurs when some of the specified combinations of dimension values do not correspond to series contained by the root metric.

- Specify affected target series
- Use this option when you know specific targets that are affected by the set of root
series and you want to investigate the effects on those targets only. By default, targets
that are affected by each root series are automatically determined. You can specify the
breadth of the series that are affected by each individiual scenario with settings on the
Options tab.
You specify target series by adding an entry to the grid for the metric field that contains the series. By default, all series that are contained in the specified metric field are included. You can customize the set of included series by customizing the included values for one or more of the dimension fields. To customize the dimension values that are included, click the ellipsis button for the dimension that you want. This action opens the Select Dimension Values dialog.

The Series Count column displays the number of sets of dimension values that are currently specified for the associated target metric. The displayed value can be larger than the actual number of affected target series (one series per set). This condition occurs when some of the specified combinations of dimension values do not correspond to series contained by the associated target metric.

- Scenario ID prefix
- Each scenario group must have a unique prefix. The prefix is used to construct an identifier that is displayed in output that is associated with each individual scenario in the scenario group. The identifier for an individual scenario is the prefix, followed by an underscore, followed by the value of each dimension field that identifies the root series. The dimension values are separated by underscores. There are no restrictions, other than uniqueness, on the value of the prefix.
- Expression for scenario values for root series
- Scenario values for a scenario group are specified by an expression, which is then used
to compute the values for each of the root series in the group. You can enter an
expression directly or click the calculator button and create the expression from the
Scenario Values Expression Builder.
- The expression can contain any target or input in the model system.
- When the scenario period extends beyond the existing data, the expression is applied to forecasted values of the fields in the expression.
- For each root series in the group, the fields in the expression specify time series
that are defined by those fields and the dimension values that define the root series.
It is those time series that are used to evaluate the expression. For example, if a
root series is defined by
`region='north'`

and`brand='X'`

, then the time series that are used in the expression are defined by those same dimension values.

As an example, assume that the root metric field is

`advertising`and that there are two dimension fields`region`and`brand`. Also, assume that the scenario group includes all combinations of the dimension field values. You might then specify`advertising*1.2`

as the expression to investigate the effect of increasing`advertising`by 20 percent for each of the time series that are associated with the`advertising`field.