# Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection

arXiv: https://arxiv.org/abs/1805.05383 · HTML: https://mlsanity.com/paper/1805.05383/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2018.

## Built on by (most cited first, 13 of 13)

- [Robust and Scalable Bayesian Online Changepoint Detection](https://mlsanity.com/paper/2302.04759/) (2023)
- [WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data](https://mlsanity.com/paper/2201.07125/) (2022)
- [Memory-free Online Change-point Detection: A Novel Neural Network Approach](https://mlsanity.com/paper/2207.03932/) (2022)
- [A unifying framework for generalised Bayesian online learning in non-stationary environments](https://mlsanity.com/paper/2411.10153/) (2024)
- [Creation and evaluation of timelines for longitudinal user posts](https://mlsanity.com/paper/2303.05891/) (2023)
- [Latent Neural Stochastic Differential Equations for Change Point Detection](https://mlsanity.com/paper/2208.10317/) (2022)
- [Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processes](https://mlsanity.com/paper/2304.00232/) (2023)
- [EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data](https://mlsanity.com/paper/2107.02463/) (2021)
- [Adaptive Resources Allocation CUSUM for Binomial Count Data Monitoring with Application to COVID-19 Hotspot Detection](https://mlsanity.com/paper/2208.05045/) (2022)
- [Greedy online change point detection](https://mlsanity.com/paper/2308.07012/) (2023)
- [RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection](https://mlsanity.com/paper/2407.09698/) (2024)
- [Stream Assembly Is an Uncontrolled Treatment in Streaming Intrusion-Detection Benchmarks](https://mlsanity.com/paper/2605.24696/) (2026)
- [High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability](https://mlsanity.com/paper/2609.24278/) (2026)

## Abstract (arXiv)

Bayesian On-line Changepoint Detection is extended to on-line model selection and non-stationary spatio-temporal processes. We propose spatially structured Vector Autoregressions (VARs) for modelling the process between changepoints (CPs) and give an upper bound on the approximation error of such models. The resulting algorithm performs prediction, model selection and CP detection on-line. Its time complexity is linear and its space complexity constant, and thus it is two orders of magnitudes faster than its closest competitor. In addition, it outperforms the state of the art for multivariate data.