Effects of different trends on moving cut data-approximate entropy
不同趋势对滑动移除近似熵的影响

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.