Composite Multiscale Partial Cross-Sample Entropy Analysis for Quantifying Intrinsic Similarity of Two Time Series Affected by Common External Factors
| Autor: | Baogen Li, Guo-Sheng Han, Zu-Guo Yu, Shan Jiang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
composite multiscale partial cross-sample entropy (CMPCSE)
Composite number General Physics and Astronomy lcsh:Astrophysics multiscale cross-sample entropy (MCSE) stock indices 01 natural sciences Stock market index Article lcsh:QC1-999 010305 fluids & plasmas Sample entropy Hang 0103 physical sciences lcsh:QB460-466 Entropy (information theory) lcsh:Q Statistical physics Composite index time series 010306 general physics lcsh:Science lcsh:Physics Mathematics |
| Zdroj: | Entropy, Vol 22, Iss 1003, p 1003 (2020) Entropy Volume 22 Issue 9 |
| ISSN: | 1099-4300 |
| Popis: | In this paper, we propose a new cross-sample entropy, namely the composite multiscale partial cross-sample entropy (CMPCSE), for quantifying the intrinsic similarity of two time series affected by common external factors. First, in order to test the validity of CMPCSE, we apply it to three sets of artificial data. Experimental results show that CMPCSE can accurately measure the intrinsic cross-sample entropy of two simultaneously recorded time series by removing the effects from the third time series. Then CMPCSE is employed to investigate the partial cross-sample entropy of Shanghai securities composite index (SSEC) and Shenzhen Stock Exchange Component Index (SZSE) by eliminating the effect of Hang Seng Index (HSI). Compared with the composite multiscale cross-sample entropy, the results obtained by CMPCSE show that SSEC and SZSE have stronger similarity. We believe that CMPCSE is an effective tool to study intrinsic similarity of two time series. |
| Databáze: | OpenAIRE |
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