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Multiscale horizontal visibility entropy: Measuring the temporal complexity of financial time series

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  • Zhao, Xiaojun
  • Zhang, Pengyuan

Abstract

In this paper, we propose a horizontal visibility entropy (HVE) to measure the temporal complexity of non-stationary financial time series. It is a graphic and information-theoretic approach. By transforming the time series into a horizontal visibility graph (HVG), the dynamics of the underlying systems can be traced out through the topology structure of the graph. Then, we use a multiscale analysis of the Shannon entropy to characterize the complexity of time series under a range of time scales. The new method is free of any pre-requisite of additional parameters on estimating the probability distribution of continuous time series, which, therefore, has the advantages of robustness, high repeatability, and low requirement on the data length. We also compare our new method with the permutation entropy (PE), and apply it to financial time series analysis, which gives some new results.

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  • Zhao, Xiaojun & Zhang, Pengyuan, 2020. "Multiscale horizontal visibility entropy: Measuring the temporal complexity of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315250
    DOI: 10.1016/j.physa.2019.122674
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    References listed on IDEAS

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    Cited by:

    1. Lahmiri, Salim & Bekiros, Stelios, 2020. "Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Chen, Yu & Ling, Guang & Song, Xiangxiang & Tu, Wenhui, 2023. "Characterizing the statistical complexity of nonlinear time series via ordinal pattern transition networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).

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