  Due to accessible big data collections from consumers, products, and stores,
advanced sales forecasting capabilities have drawn great attention from many
companies especially in the retail business because of its importance in
decision making. Improvement of the forecasting accuracy, even by a small
percentage, may have a substantial impact on companies' production and
financial planning, marketing strategies, inventory controls, supply chain
management, and eventually stock prices. Specifically, our research goal is to
forecast the sales of each product in each store in the near future. Motivated
by tensor factorization methodologies for personalized context-aware
recommender systems, we propose a novel approach called the Advanced Temporal
Latent-factor Approach to Sales forecasting (ATLAS), which achieves accurate
and individualized prediction for sales by building a single
tensor-factorization model across multiple stores and products. Our
contribution is a combination of: tensor framework (to leverage information
across stores and products), a new regularization function (to incorporate
demand dynamics), and extrapolation of tensor into future time periods using
state-of-the-art statistical (seasonal auto-regressive integrated
moving-average models) and machine-learning (recurrent neural networks) models.
The advantages of ATLAS are demonstrated on eight product category datasets
collected by the Information Resource, Inc., where a total of 165 million
weekly sales transactions from more than 1,500 grocery stores over 15,560
products are analyzed.
