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Characterization of autoregressive processes using entropic quantifiers

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  • Traversaro, Francisco
  • Redelico, Francisco O.

Abstract

The aim of the contribution is to introduce a novel information plane, the causal-amplitude informational plane. As previous works seems to indicate, Bandt and Pompe methodology for estimating entropy does not allow to distinguish between probability distributions which could be fundamental for simulation or for probability analysis purposes. Once a time series is identified as stochastic by the causal complexity-entropy informational plane, the novel causal-amplitude gives a deeper understanding of the time series, quantifying both, the autocorrelation strength and the probability distribution of the data extracted from the generating processes. Two examples are presented, one from climate change model and the other from financial markets.

Suggested Citation

  • Traversaro, Francisco & Redelico, Francisco O., 2018. "Characterization of autoregressive processes using entropic quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 13-23.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:13-23
    DOI: 10.1016/j.physa.2017.07.025
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    1. Fernandes, Leonardo H.S. & de Araujo, Fernando H.A. & Tabak, Benjamin M., 2021. "Insights from the (in)efficiency of Chinese sectoral indices during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).

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    Keywords

    Permutation entropy; Time series analysis;

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