Bayesian Intermittent Demand Forecasting for Large Inventories

Part of Advances in Neural Information Processing Systems 29 (NIPS 2016)

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Matthias W. Seeger, David Salinas, Valentin Flunkert


We present a scalable and robust Bayesian method for demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced to linear-time Kalman smoothing, which allows us to operate on several orders of magnitude larger problems than previous related work. In a study on large real-world sales datasets, our method outperforms competing approaches on fast and medium moving items.