2018/05/14 by Jeremias Knoblauch, Theodoros Damoulas, Knoblauch, Jeremias +1 · 1 citation
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Data Analysis with R #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Statistical Methods and Inference #cs.LG #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.1805.05383
10 pages, 7f figures, to appear in Proceedings of the 35th International Conference on Machine Learning 2018
openalex publication_date 2018/05/14 · openalex created_date 2018/06/01 · arxiv created 2018/06/06 · arxiv updated 2018/06/07 · openalex updated_date 2026/07/28
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.