If you have run any pipelines, they will need to be run again after changing these settings.
CAUTION
Changing any of these project settings in an external forecast project can lead to errors.
Specify the number of periods to forecasta numerical prediction of a future value for a specified time period for each unique combination of BY variable values (also called the horizonthe number of intervals into the future, beyond a base date, for which analyses and predictions are made.). For example, if the time interval for this project is set to WEEK, specify 12 to generate 12 weeks of forecasts for the dependent variable.
Specify a positive integer for the number of time periods to leave out when fitting models. The number of periods to exclude from modeling is also known as the out-of-sample region.
This out-of-sample regionthe number of time periods before the end of the data that are removed when fitting models. After model selection, forecasts are generated in the out-of-sample region and then compared to the actual data to determine accuracy. is used to determine performance statistics for the models selected for each time seriesan aggregation of transactional data into specified time intervals and sorted according to unique combinations of the default attributes (BY variables). Removing the most recent time periods provides a better assessment on how well the model generalizes, or how well it performs on data that was not used to fit or select the model.
Forecasts start in the out-of-sample region. If the out-of-sample region is greater than or equal to the horizon, no forecasts are made in the future periods after the end of the historical data. For more information, see Using an Out-of-Sample Region.
If the project has been imported or upgraded from previous versions of SAS Visual Forecasting, it might not show any statistics for an out-of-sample region, if one is specified. For more information, see Imported Project Does Not Show Out-of-Sample Region.
Specify the size of the confidence level for the forecasts. By default, this confidence level is 0.05, which is a 95% confidence limit.
Choose the statistics of fit analysis that is used to determine the champion pipeline for this project. The statistics of fit are statistical values that are used to evaluate how well a forecasting model performs by comparing the actual data to the predictions. For a given forecast model that has been fitted to the time series data, the model should be checked or evaluated to see how well it fits or forecasts the data.
Each modeling node in a pipeline has its own model selection setting to determine the best model to use from within that node. The Champion selection criteria is used to select the best pipeline when more than one pipeline is run.
For a full description of the statistics of fit options that are available, see Model Selection Criteria.
Select this option to allow negative values for forecasts and overrides. If you clear this check box, then any negative values in the forecast model are set to 0 and any overrides with negative values are removed.
This setting is turned off by default for time series projects. For external forecasts, this setting is turned on by default.
If Missing interpretation for the dependent variable is set to 0, you could still see some negative values for the lower confidence limitsthe upper and lower values of a (usually 95%) confidence interval. In repeated sampling, approximately (1-alpha) 100% of the resulting intervals would contain the true value of the parameter that the interval estimates (where alpha is the confidence level associated with the interval). (Forecast LCL) in the project time plots.
Changes to this setting invalidate any existing overrides that have been created. Changes also invalidate the pipelines.