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Bayesian Intermittent Demand Forecasting for Large Inventories
Matthias W Seeger · David Salinas · Valentin Flunkert

Tue Dec 06 02:40 AM -- 03:00 AM (PST) @ Area 1 + 2

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.

Author Information

Matthias W Seeger (Amazon)
David Salinas (Amazon)
Valentin Flunkert (Amazon)

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