Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
Machine Learning
2015-03-20 v2 Machine Learning
Methodology
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.
Cite
@article{arxiv.1203.0453,
title = {Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation},
author = {Song Liu and Makoto Yamada and Nigel Collier and Masashi Sugiyama},
journal= {arXiv preprint arXiv:1203.0453},
year = {2015}
}