Dan Kalifa, M.Sc. Thesis Seminar
Advisor: Dr. Kira Radinsky
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 will help overcome the challenge of prediction under anomalies. We mine a large repository of 40 years of world events and their textual representations. We present a novel methodology based on transformers to construct an embedding of a day based on the causal subgraph 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 one of the world’s largest online marketplaces. We show over numerous categories that our method outperforms state-of-the-art baselines during anomalies.