Open AccessStochastially re-sorting detrended fluctuation analysis: a new method to define the threshold of extreme event
Author(s) -
Hou Wei,
Zhang Da-Quan,
Zhou Yun,
Ping Yang
Publication year - 2011
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.60.109202
Subject(s) - detrended fluctuation analysis , computer science , surrogate data , extreme value theory , percentile , sorting , statistics , event (particle physics) , heuristic , mathematics , algorithm , artificial intelligence , physics , nonlinear system , quantum mechanics , geometry , scaling
By combining detrended fluctuation analysis (DFA) method with surrogate data method, and using the Heuristic segmentation algorithm as well as Chi-Square statistics, we develop a new method to determine the threshold of extreme events, e.g. stochastically re-sorting detrended fluctuation analysis (S-DFA) method. The S-DFA method has a certain phsical background, when the occurrence rate of the data is small, then these data belong to little-probability events and they contain so little information about the dynamic system, the states corresponding to these data are abnormal states or extreme states of the system. When the occurrence rate of the data is large or even in distribution these data do not belong to little-probability events and they contain much information about the system, the states corresponding to these data are normal states of the system. Compared with the Percentile curves method, the S-DFA method gives the critical value between extreme event and non-extreame event, which is definite and unique. We also extensively validate the effectiveness of S-DFA method through extreme event detection.