2018
ICML
ICML 2018
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Abstract
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.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— vector autoregression
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
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Trend Setter
— Stochastic Processes
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Hot Topic Early Bird
— model selection
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Stochastic Processes
Data Science & Analytics > Methods > Time Series Analysis
Machine Learning > Learning Types > Online Learning
Mathematics & Optimization > Mathematics > Stochastic Processes
Machine Learning > Bayesian & Probabilistic > Bayesian Inference