  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.
