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arXiv · 1709.07638

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale

Matthias Seeger·Syama Rangapuram·Yuyang Wang·David Salinas·Jan Gasthaus·Tim Januschowski·Valentin Flunkert
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Abstract

We present a scalable and robust Bayesian inference method for linear state space models. The method is applied to 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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Matthias Seeger, Syama Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert. 2017-09-22. Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. https://arxiv.org/abs/1709.07638

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