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arXiv · 1203.0453

Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation

Song Liu·Makoto Yamada·Nigel Collier·Masashi Sugiyama
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Abstract

The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on artificial and real-world datasets including human-activity sensing, speech, and Twitter messages, we demonstrate the usefulness of the proposed method.

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Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama. 2013-01-16. Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation. https://doi.org/10.1016/j.neunet.2013.01.012

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