Oracle Corporation
Auto Clustering Prediction Models

Abstract:

Embodiments predict future demand for a first product by receiving historical sales data for an aggregate products/locations level, the historical sales data including a plurality of sales data points, including sales data points for the first product at each of a plurality of locations. Embodiments extract a plurality of different types of features related to sales of each of the products and generate a plurality of clusters of sales data points based on the plurality of different types of features. Embodiments train each of the clusters to generate a plurality of trained cluster models including promotion effects per cluster. For a particular time period, a particular location and the first product, embodiment identify the features for the time period and map to one of the trained cluster models to fetch the promotion effects for the time period. Embodiments then use the promotion effects to forecast demand for the first product.