Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
Machine Learning
2017-09-25 v1 Machine Learning
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.
Cite
@article{arxiv.1709.07638,
title = {Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale},
author = {Matthias Seeger and Syama Rangapuram and Yuyang Wang and David Salinas and Jan Gasthaus and Tim Januschowski and Valentin Flunkert},
journal= {arXiv preprint arXiv:1709.07638},
year = {2017}
}