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

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