Open AccessA new approach to abrupt change detection based on change of probability density distribution
Author(s) -
Cheng Hai-Ying,
Wenping He,
Tao He,
Qiong Wu
Publication year - 2012
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.61.039201
Subject(s) - kurtosis , skewness , independence (probability theory) , probability density function , series (stratigraphy) , index (typography) , change detection , variable (mathematics) , structural change , statistical physics , computer science , statistics , mathematics , physics , mathematical analysis , artificial intelligence , geology , paleontology , world wide web , economics , macroeconomics
For a stable dynamic system, probability density distribution (PDD) of a system variable is relatively stable, and if there is a change in dynamic structure of a system, the PDD of the system variable will have some change correspondingly. According to this characteristic of PDD of a dynamic system, in this paper we present two new methods, namely, skewness index and kurtosis index, to detect an abrupt change in a time series by means of identifying some small changes in PDD. Tests on model time series indicate that skewness index and kurtosis index can be used to identify an abrupt change, such as abrupt change in parameter of an equation and abrupt dynamic change. Thus, we provide a new approach to detecting abrupt change in time series based on PDD. Further studies show that the detected results of the skewness index and kurtosis index are almost independence of the length of a subseries.