Conference Proceedings

Demand Forecast in E-commerce Using a Long Short-Term Memory Neural Network Methodology

Kasun Bandara, Peibei Shi, Christoph Bergmeir, Hansika Hewamalage, Tran Quoc, Brian Seaman, T Gedeon (ed.), KW Wong (ed.), M Lee (ed.)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | SPRINGER INTERNATIONAL PUBLISHING AG | Published : 2019

Abstract

Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series are available, conditioning the forecast of an individual time series on past behaviour of similar, related time series can be beneficial. Since the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model..

View full abstract