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

arXiv: https://arxiv.org/abs/1709.07638 · HTML: https://mlsanity.com/paper/1709.07638/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures](https://mlsanity.com/paper/2110.13179/) (2021)
- [From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba](https://mlsanity.com/paper/2109.08381/) (2021)
- [The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems](https://mlsanity.com/paper/2409.16098/) (2024)
- [Dynamic Temporal Reconciliation by Reinforcement learning](https://mlsanity.com/paper/2201.11964/) (2022)
- [Adaptive Interventions for Global Health: A Case Study of Malaria](https://mlsanity.com/paper/2303.02075/) (2023)

## Abstract (arXiv)

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.