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

Understand

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.