The weighting transformation is useful in a wide variety of situations. This section briefly notes some of them. Its purpose is to indicate some directions you may want to take with your own business data.
Trading day corrections. Many businesses are sensitive to the number of working days per period. Consider a service provider who is closed on the weekends. The number of working days in January (and all other months) will vary from year to year depending on how many weekend days happen to fall in any given month. If the number of working days has an impact on sales, then it needs to be accounted for in the model. A simple solution would be to use a weighting transformation where the weights consist of the number of working days per month.
User-defined seasonality. At times you may wish to supply your own estimate of the seasonal pattern rather than trying to extract it directly from the data. This might be desirable if the data were short or very noisy. The weighting variable would consist of seasonal multipliers for the series.
Product phase outs and other forecast adjustments. There may be times where you wish to alter the statistical forecasts using a weighting variable rather than the forecast adjustment facility. For example, let’s say that you plan on discontinuing a product and wish to use the statistical forecast until the product is discontinued. You could create a weighting variable that consists of all ones during the history and the forecast periods prior to the discontinuation date and equals zero for all periods thereafter. If the product would be phased out over a three month period rather than ending abruptly, then you could use weights like 0.75, 0.5 and 0.25 during the phase out period.
This concludes the Using Weighting Transformations tutorial.