Abstract
Methods for dealing with seasonal patterns of product sales can be categorized into two groups: those that forecast the demand for seasonal products by estimating the individual seasonal components for each product, and those that estimate the seasonal component by combining ‘similar’ products into a product line. An approach is proposed for the latter case, based on a synthesis of time series decomposition techniques and cluster analysis. Some initial experiments on a sample of retail sales data demonstrate its feasibility and give some comparative insights into this and alternative methods.
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