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Bayesian Intermittent Demand Forecasting for Large Inventories

Matthias W Seeger · David Salinas · Valentin Flunkert

Area 5+6+7+8 #192

Keywords: [ Time Series Analysis ] [ Gaussian Processes ] [ Graphical Models ] [ Variational Inference ] [ Large Scale Learning and Big Data ] [ (Other) Statistics ]


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.

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