Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly
This addresses the challenge of reliable sales forecasting for e-commerce applications during unpredictable events, though it appears incremental as it builds on existing transformer methods with new event data.
The paper tackles the problem of predicting e-commerce consumer demand during anomalous periods like pandemics or sports events by leveraging world event data, showing that their transformer-based method outperforms state-of-the-art baselines during anomalies across numerous product categories.
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.