Composite Multiscale Partial Cross-Sample Entropy Analysis for Quantifying Intrinsic Similarity of Two Time Series Affected by Common External Factors.

摘要: In this paper, we propose a new cross-sample entropy, namely the composite multiscale partial entropy (CMPCSE), for quantifying intrinsic similarity of two time series affected by common external factors. First, in order to test validity CMPCSE, apply it three sets artificial data. Experimental results show that CMPCSE can accurately measure simultaneously recorded removing effects from third series. Then is employed investigate Shanghai securities index (SSEC) and Shenzhen Stock Exchange Component Index (SZSE) eliminating effect Hang Seng (HSI). Compared with obtained SSEC SZSE have stronger similarity. We believe an effective tool study