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Abstract

Various trends exist in many observation data, such as periodical trend caused by seasonal variation, linear trend and polynomial trend brought about by global warming. In the present paper, the effects of different trends on moving cut data-approximate entropy are investigated. The numerical tests on model time series indicate that the detection results of moving cut data-approximate entropy are little affected by periodical trend, linear trend and nonlinear trend. The reliability of abrupt change detection of moving cut data-approximate entropy is demonstrated, which provides an experimental basis for the wide applications of the present method in real observation data.

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Corresponding author:He Wen-Ping,wenping_he@163.com

Funds:Project supported by the National Basic Research Program of China (Grant No. 2012CB955203), the National Natural Science Foundation of China (Grant Nos. 40930952, 41175067, 40905034), and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant Nos. 2007BAC29B01, 2009BAC51B04).

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Vol. 61, Issue 6 - 2012-03-20

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