Forecast Method (Explorer)

Syntax

Report.Forecast

Applies To

Report Object

TERMS OF USE

The forecasting methods utilized in the Forecasting Function are based on the statistical analysis of historical information drawn from underlying data sources. The accuracy of the forecasted values is subject to many variables, including the accuracy of the underlying historical data and external events which could affect the validity of that underlying historical data for forecasting purposes. The Forecasting Function is to be used only as a guide of the future values for the measures being forecasted and is not intended to be used as the basis for complex financial or business decisions.

IBM® makes no representations as to the accuracy of the actual future values and does not guarantee any specific results. You use the Forecasting Function and the data it generates at your own risk. The Forecasting Function may contain errors or produce inaccurate calculations. You accept the Forecasting Function and the documentation "AS IS". IN NO EVENT SHALL IBM BE LIABLE FOR DAMAGES OF ANY KIND INCLUDING, WITHOUT LIMITATION, DIRECT, INDIRECT, INCIDENTAL, PUNITIVE OR CONSEQUENTIAL DAMAGES, RESULTING FROM THE USE OF THE FORECASTING FUNCTION OR THE INTERPRETATION OF THE DATA RESULTING THEREFROM.

Discussion

This method is available only if the Report object is in Explorer mode (the ReporterMode property is False).

You can use one of the following time series forecasting methods:

• The Trend forecasting method is based on the linear regression technique of time series forecasting. Trend forecasting gives the best forecasting reliability when the driving factors of your business affect your measures in a linear fashion. Use the Trend forecasting method when you have only two data values representing two time periods in your historic data.
• The Growth forecasting method is based on the exponential regression technique of time series forecasting. Growth forecasting gives you the best forecasting reliability when the driving factors of your business affect your measures exponentially.
• The Autoregression forecasting method is based on the auto-correlational approach to time series forecasting. Autoregression forecasting detects the linear, non-linear, and seasonal fluctuations in historical data and projects these trends into the future. Autoregression provides the best forecasting reliability when the driving factors underlying your business are affected by seasonal fluctuations. Use the Autoregression method when you have historic data representing a large number of time periods (for example, more than 24 monthly periods).

The Rows or Columns must contain time categories.

The forecast horizon is limited to the number of time categories in the report. For example, if there are two year categories in the report (2008, 1997), the maximum horizon value is two.

If the horizon value is zero, existing forecasts are removed from the report.

Return Type

Nothing

Example

This example returns an open report, confirms that the report is in Explorer mode, verifies that either the Column objects or the Row objects contain time categories, verifies that the horizon value is valid, and enters a forecast horizon value of two.

Sub Main()

   Dim objPPRep As Object

   Set objPPRep = CreateObject ("CognosPowerPlay.Report")

   objPPRep.Open ("C:\Great Outdoors.mdc")

   objPPRep.ExplorerMode = True

   objPPRep.Visible = True

   objPPRep.Forecast 1,2

   Set objPPRep = Nothing

End Sub

Related Topics

Report Object