SAP ABAP Data Element UPF_Y_FC_STR (Forecast Strategy)

Hierarchy

☛ SAP_BW (Software Component) SAP Business Warehouse
   ⤷ BW-PLA-BPS (Application Component) Business Planning and Simulation
     ⤷ UPF (Package) SEM-BPS: Planning Functions

Basic Data

Data Element	UPF_Y_FC_STR
Short Description	Forecast Strategy

Data Type

Category of Dictionary Type	D	Domain
Type of Object Referenced		No Information
Domain / Name of Reference Type	UPF_D_FC_STR
Data Type	CHAR	Character String
Length	2
Decimal Places	0
Output Length	2
Value Table

Further Characteristics

Search Help: Name
Search Help: Parameters
Parameter ID
Default Component name
Change document	☐
No Input History	☐
Basic direction is set to LTR	☐
No BIDI Filtering	☐

Field Label

	Length	Field Label
Short	10	Fore.Strat
Medium	20	Forecast Strategy
Long	30	Forecast Strategy
Heading	30	Forecast Strategy

Documentation

Definition

The forecasting strategy defines the way in which forecast values will be computed. The following forecasting strategies are available:

• Average
• Moving average
• Weighted moving average
• Simple exponential smoothing (constant model)
• Linear exponential smoothing (trend model)
• Seasonal exponential smoothing (season model)
• Trend seasonal exponential smoothing (multiplicative seasonal components)
• Trend seasonal exponential smoothing (additive seasonal components)
• Linear regression
• Automatic model selection

Use

All forecasting strategies are based on statistical forecasting techniques and therefore on forecasting models that calculate the time series of historical values mathematically. If you expect the development of historical values to proceed as it has before, choose a forecasting model that fits the trend of the time series well.

The automatic model selection strategy allows you to let the system choose the forecasting model that best fits the trend of historical values (see below for details).

• Average
  The forecast value is calculated from the mean of the historic values.
• Moving average
  The forecast value is calculated according to the order.
• Weighted moving average
  When the moving average is calculated, each historic value obtains the weighting defined in the weighting group.
• Simple exponential smoothing (constant model)
  Exponential smoothing is appropriate where the historic data essentially follows a horizontal trend.
• Linear exponential smoothing (trend model)
  Forecasting is based on Holt's method and is appropriate where the historic values essentially follow an increasing or decreasing trend.
• Seasonal exponential smoothing (season model)
  Choose this strategy if your historic values display seasonal fluctuations (for example annual) from a constant basic value.
• Trend seasonal exponential smoothing (multipl. seasonal component)
  Forecasting is based on Winter/Holt's multiplicative method and is appropriate if historic values fluctuate on a seasonal basis from an increasing or decreasing trend. The magnitude of the fluctuation is dependent on the size of the trend.
• Trend seasonal exponential smoothing (additive seasonal components)
  Forecasting is based on Winter/Holt's additive method and is appropriate if historic values fluctuate on a seasonal basis from an increasing or decreasing trend. The magnitude of the fluctuation is not dependent on the size of the trend.
• Linear regression
  Simple linear regression (method of smallest quadrats).
• Automatic model selection
  This strategy compares the results from all four exponential smoothing techniques and chooses the forecasting model that is best suited. Note that the Optimization of smoothing factors is is suggested by the system.

Dependencies

Example

History

Last changed by/on	SAP	20130604
SAP Release Created in	350