Premium
A new method for abrupt dynamic change detection of correlated time series
Author(s) -
He Wenping,
Feng Guolin,
Wu Qiong,
He Tao,
Wan Shiquan,
Chou Jifan
Publication year - 2012
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.2367
Subject(s) - detrended fluctuation analysis , approximate entropy , series (stratigraphy) , time series , computer science , autocorrelation , moving average , entropy (arrow of time) , change detection , statistics , mathematics , algorithm , artificial intelligence , geology , paleontology , physics , geometry , quantum mechanics , scaling
On the basis of detrended fluctuation analysis (DFA), a new method, moving cut data‐DFA (MC‐DFA), was presented to detect abrupt dynamic change in correlated time series. The numerical tests show the capability of the presented method to detect abrupt change time‐instants in model time series generated by Logistic map. Moving DFA (MDFA) and approximate entropy (ApEn) can provide some information such as a single time‐instant of abrupt dynamic change, but both of them cannot exactly detect all of the abrupt change regions. Some traditional methods, such as moving t ‐test, Cramer method, Mann–Kendall test and Yamamoto method, even cannot provide any information of abrupt dynamic change in these model time series. Meanwhile, results showed that windows sizes and strong noise have some less effect on the MC‐DFA results. In summary, MC‐DFA provides a reliable measure to detect the abrupt dynamic change in correlated time series, and perfectively make up the deficiencies of MDFA and ApEn. The applications in daily surface air pressure records further verify the validity of the present method. Copyright © 2011 Royal Meteorological Society