Disjoint-Support Factors and Seasonality Estimation in E-Commerce

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 10536; pp. 77 - 88
• Main Author: Jha, Abhay
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 9783319712727; 3319712721
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-71273-4_7

Successful inventory management in retail entails accurate demand forecasts for many weeks/months ahead. Forecasting models use seasonality: recurring pattern of sales every year, to make this forecast. In e-commerce setting, where the catalog of items is much larger than brick and mortar stores and hence includes a lot of items with short history, it is infeasible to compute seasonality for items individually. It is customary in these cases to use ideas from factor analysis and express seasonality by a few factors/basis vectors computed together for an entire assortment of related items. In this paper, we demonstrate the effectiveness of choosing vectors with disjoint support as basis for seasonality when dealing with a large number of short time-series. We give theoretical results on computation of disjoint support factors that extend the state of the art, and also discuss temporal regularization necessary to make it work on walmart e-commerce dataset. Our experiments demonstrate a marked improvement in forecast accuracy for items with short history.