mlsanityData to 14 Oct 2026

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

arXiv 1805.05383 ↗First posted 14 May 2018stat.ML cs.LG stat.MEcited by 13

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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.

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

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Citing papers per year: 2021: 1; 2022: 4; 2023: 4; 2024: 2; 2025: 0; 2026: 2.

13 papers.

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