Embodiments predict/forecast demand of a product by receiving historical sales data for the product and, using a plurality of different seasonality estimation methods, estimating a plurality of different seasonality estimations for future time periods and determining an approximate error amount for each of the different seasonality estimations. Embodiments determine a weight for each of the plurality of different seasonality estimation methods based on the corresponding approximate error amount and generate an aggregate seasonality model based on the plurality of different seasonality estimations and the weights. Embodiments then determine a demand forecast using the aggregate seasonality model.
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