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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Vydáno v:	Neural networks Ročník 43; s. 72 - 83
Hlavní autoři:	Liu, Song, Yamada, Makoto, Collier, Nigel, Sugiyama, Masashi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 01.07.2013 Elsevier
Témata:	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 Change-point detection Kernel methods Time-series data Distribution comparison Relative density-ratio estimation Statistical analysis Divergence Event detection Time series Non parametric estimation Social network Change point Kernel method Lying Human activity Probability density function Web site
ISSN:	0893-6080, 1879-2782, 1879-2782
On-line přístup:	Získat plný text
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