Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly

TL;DR

This paper introduces a transformer-based approach that leverages 40 years of world event data to improve e-commerce demand forecasting, especially during anomalous periods like pandemics or weather events.

Contribution

The paper presents a novel methodology using world event embeddings to enhance demand prediction accuracy during anomalies, outperforming existing models.

Findings

Method outperforms state-of-the-art baselines during anomalies

Leveraging external world event data improves forecasting accuracy

Transformers effectively embed event relations for demand prediction

Abstract

Consumer demand forecasting is of high importance for many e-commerce applications, including supply chain optimization, advertisement placement, and delivery speed optimization. However, reliable time series sales forecasting for e-commerce is difficult, especially during periods with many anomalies, as can often happen during pandemics, abnormal weather, or sports events. Although many time series algorithms have been applied to the task, prediction during anomalies still remains a challenge. In this work, we hypothesize that leveraging external knowledge found in world events can help overcome the challenge of prediction under anomalies. We mine a large repository of 40 years of world events and their textual representations. Further, we present a novel methodology based on transformers to construct an embedding of a day based on the relations of the day's events. Those embeddings are then used to forecast future consumer behavior. We empirically evaluate the methods over a large e-commerce products sales dataset, extracted from eBay, one of the world's largest online marketplaces. We show over numerous categories that our method outperforms state-of-the-art baselines during anomalies.