Change-point detection in time-series data by relative density-ratio estimation

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...

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Bibliographic Details
Published in:Neural networks 2013-07, Vol.43, p.72-83
Main Authors: Liu, Song, Yamada, Makoto, Collier, Nigel, Sugiyama, Masashi
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Change-point detection
Computer science
control theory
systems
Data processing. List processing. Character string processing
Distribution comparison
Divergence
Exact sciences and technology
Gene Expression Profiling - methods
Humans
Inference from stochastic processes
time series analysis
Kernel methods
Mathematics
Memory organisation. Data processing
Messages
Models, Statistical
Neural networks
Probability and statistics
Relative density-ratio estimation
Retrospective Studies
Samples
Sciences and techniques of general use
Segments
Software
Specific Gravity
Speech
Statistical analysis
Statistics
Time Factors
Time-series data
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Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2013.01.012