AI & ML
Time Series Change Point Detection with Gaussian Processes
arXiv stat.ML0 views
arXiv:2611.13295v1 Announce Type: new Abstract: This paper proposes a novel general Gaussian process framework for change point detection in time series. A change is defined as an alteration of the hyperparameters of the Gaussian process prior. We formulate the problem in the context of statistical hypothesis testing and demonstrate its performance over both offline and online multiple change points detection. The approach provides theoretical guaranties of its performance. An important advantage of the developed framework is that it does not need a detailed specification of the time series function before and after a change. The hyperparameter values are learned from the data. Comprehensive evaluation of the proposed framework is provided both on synthetic and real data and its efficiency is demonstrated.
Source：https://arxiv.org/abs/2611.13295PreviousContinuous Horizon ForecastingNextVC-Dimension and Generalization in Geometric Machine Learning with Applications to Active Learning
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