Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
This addresses the problem of identifying abrupt changes in time-series for applications such as activity sensing and social media analysis, representing an incremental improvement with a new statistical method.
The paper tackles change-point detection in time-series data by proposing a novel algorithm based on non-parametric divergence estimation between segments, using the relative Pearson divergence and direct density-ratio estimation, and demonstrates its usefulness on artificial and real-world datasets like human-activity sensing, speech, and Twitter messages.
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.