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Seasonal Linear Regression ( RELNAPO_30A_SP1_FCS_SLR )

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Seasonal Linear Regression

Scope of Functions

APO Release 3.0A (First Customer Shipment and Support Package 1) comes with a new forecast strategy for seasonal linear regression, forecast strategy 35. Forecast strategy 35 can be used as an alternative to forecast strategies 30 and 31, which return large basic values if the seasonal index is zero or nearly zero. Do not use strategy 35 if your historical data has strong trend patterns.

The system calculates the seasonal linear regression line as follows:

1. The seasonal indices are calculated:

Determination of the starting seasonal index for each historical period t

1. The number nk of seasons available within the whole historical time series is calculated:

nk = ntotal / nseason

where nseason is the number of periods per season and ntotal is the total number of historical values.

1. The average value Ak of each season k is calculated:

Ak = sum of V(t) / nseason

where V(t) is the historical value of period t and nseason is the number of periods per season.

1. The starting seasonal index sstart (t) is calculated for each period t within each season.

Sstart (t) = V(t) / Ak

If a non-completed season existsthat is, if nk is not an integer number the starting seasonal index sstart (t) of the oldest historical data is calculated with the average Ak of the nkth season.

Determination of the average seasonal index

1. If k complete seasons are available, the starting seasonal indices are averaged:

saverage(s) = (sstart (s) + sstart(nseason + s) + ....+sstart((k-1) n season+s))/k,

s = 1, ... , nseason

Smoothing of the average seasonal indices

1. If you have entered a smoothing factor in field PERSMO of the univariate forecast profile, the result of step 4 is smoothed. SAP recommends that you enter a smoothing factor of 1. If the smoothing factor is 0, no seasonal influence is calculated and only linear regression is carried out.

The actual data is corrected on the basis of the seasonal indices calculated in step 1.

Linear regression is performed on the non-seasonal actual values.

The seasonal indices are applied to the results of the linear regression calculation, which produces the forecast results.

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