/AWS1/CL_FCS=>CREATEWHATIFFORECAST()
¶
About CreateWhatIfForecast¶
A what-if forecast is a forecast that is created from a modified version of the baseline forecast. Each what-if forecast incorporates either a replacement dataset or a set of transformations to the original dataset.
Method Signature¶
IMPORTING¶
Required arguments:¶
IV_WHATIFFORECASTNAME
TYPE /AWS1/FCSNAME
/AWS1/FCSNAME
¶
The name of the what-if forecast. Names must be unique within each what-if analysis.
IV_WHATIFANALYSISARN
TYPE /AWS1/FCSARN
/AWS1/FCSARN
¶
The Amazon Resource Name (ARN) of the what-if analysis.
Optional arguments:¶
IT_TIMESERIESTRANSFORMATIONS
TYPE /AWS1/CL_FCSTIMESERIESTRANSF00=>TT_TIMESERIESTRANSFORMATIONS
TT_TIMESERIESTRANSFORMATIONS
¶
The transformations that are applied to the baseline time series. Each transformation contains an action and a set of conditions. An action is applied only when all conditions are met. If no conditions are provided, the action is applied to all items.
IO_TIMESERIESREPLCMTSDATASRC
TYPE REF TO /AWS1/CL_FCSTIMESERIESREPLCS00
/AWS1/CL_FCSTIMESERIESREPLCS00
¶
The replacement time series dataset, which contains the rows that you want to change in the related time series dataset. A replacement time series does not need to contain all rows that are in the baseline related time series. Include only the rows (measure-dimension combinations) that you want to include in the what-if forecast.
This dataset is merged with the original time series to create a transformed dataset that is used for the what-if analysis.
This dataset should contain the items to modify (such as item_id or workforce_type), any relevant dimensions, the timestamp column, and at least one of the related time series columns. This file should not contain duplicate timestamps for the same time series.
Timestamps and item_ids not included in this dataset are not included in the what-if analysis.
IT_TAGS
TYPE /AWS1/CL_FCSTAG=>TT_TAGS
TT_TAGS
¶
A list of tags to apply to the what if forecast.