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

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

2012-03-02

arxiv.org/abs/1203.0453v1

Statistics

Machine Learning

Scientific paper

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 that is based on non-parametric divergence estimation between 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, speeches, and Twitter archives, we demonstrate the usefulness of the proposed method.

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