1. A computer implemented sales history decomposition method comprising: mapping sales data related to demand forecast units to a plurality of demand forecast unit groups for one or more product categories, wherein each demand forecast unit is a unique product identifier code, wherein the sales data relates to actual sales of products, wherein each demand forecast unit is assigned to a demand forecast unit group included in the plurality of demand forecast unit groups wherein demand forecast unit groups correspond to different categories of demand forecast units; after said mapping, time slotting of the sales data; after said time slotting, aggregating the sales data contained in the plurality of demand forecast unit groups; after said aggregating, initial estimating of a trend in the aggregated sales data, wherein said initial estimating includes computing a moving average; computing using a computer processor a de-trended sales history using the trend estimate; after said computing, using a computer processor regressing the de-trended sales history as a function of price, seasonal, and marketing instrument effects; after said regressing, removing estimated price, seasonal, and marketing instrument effects from the sales data using the results of regressing the de-trended sales history; after said removing, re-estimating trend effects from the sales data from which the estimated price, seasonal, and marketing instrument effects have been removed; computing de-trended and de-seasonalized sales data using the re-estimated trend effects and the sales data from which the estimated price, seasonal, and marketing instrument effects have been removed; and outputting the computed de-trended and de-seasonalized sales data; the method further comprising repeating the steps of: computing using a computer processor de-trended sales history using the trend estimate; regressing the de-trended sales history as a function of price, seasonal, and marketing instrument effects; removing estimated price, seasonal, and marketing instrument effects from the sales data for the particular product using the results of regressing the de-trended sales history; and re-estimating trend effects from the sales data for the particular product from which the estimated price, seasonal, and marketing instrument effects have been removed, wherein aggregating the sales data includes aggregating price ratios, wherein the denominator of the price ratios is an exponentially smoothed average of historical observed prices, and wherein time slotting of the sales data includes computing a reference price.