Modified multiscale cross-sample entropy for complex time series

作者：

Highlights：

• We introduce the CMCSE and further propose the RCMCSE method.

• MCSE, CMCSE and RCMCSE methods are employed to artificial and financial time series.

• RCMCSE reduces standard deviation and the probability of inducing undefined entropy.

• RCMCSE can provide better robustness and more accurate entropies.

• RCMCSE is more applicable for the study between US and Chinese stock markets.

摘要

•We introduce the CMCSE and further propose the RCMCSE method.•MCSE, CMCSE and RCMCSE methods are employed to artificial and financial time series.•RCMCSE reduces standard deviation and the probability of inducing undefined entropy.•RCMCSE can provide better robustness and more accurate entropies.•RCMCSE is more applicable for the study between US and Chinese stock markets.

论文关键词：Refined composite multiscale cross-sample entropy (RCMCSE),Composite multiscale cross-sample entropy (CMCSE),Multiscale cross-sample entropy (MCSE),Artificial time series,Stock indices

论文评审过程：Received 16 March 2016, Revised 25 April 2016, Accepted 9 May 2016, Available online 26 May 2016, Version of Record 26 May 2016.

论文官网地址：https://doi.org/10.1016/j.amc.2016.05.013