NeurIPS 2016oral140 citations
Bayesian Intermittent Demand Forecasting for Large Inventories
Matthias W Seeger, David Salinas, Valentin Flunkert
Abstract
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.
BibTeX
@inproceedings{NIPS2016_03255088,
author = {Seeger, Matthias W and Salinas, David and Flunkert, Valentin},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Bayesian Intermittent Demand Forecasting for Large Inventories},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/03255088ed63354a54e0e5ed957e9008-Paper.pdf},
volume = {29},
year = {2016}
}