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PENTROPY: GAUSS module to compute Permutation Entropy point estimates of a time series

Author

Listed:
  • Erica Clower

    (Aptech Systems)

  • Miguel Henry

    (Greylock McKinnon Associates)

Programming Language

GAUSS

Abstract

pentropy computes Bandt and Pompe (2002) Permutation Entropy point estimates of a time series by capturing the order relations between values of a time series and extracting a probability distribution of the ordinal patterns. pentropy also computes the normalized Permutation Entropy and allows change the embedding dimension, D, and time delay, tau, parameters. pentropy uses dynamic arguments introduced by GAUSS in GAUSS 15. The file dynargs.dec is included in order to implement the dynamic arguments and can be found in your GAUSS home directory. To handle equal values in a given time series pentropy provides three methods: adding white noise with the strength of the stochastic term being smaller than the smallest distance between values (“noise”), the values get the same rank number within the regarded sequence (“equal”), and the ranks of these values are determined in accordance to their order in the sequence (“order”). A plot of the relative frequencies of admissible permutation patterns-ordinal relations is also provided.

Suggested Citation

  • Erica Clower & Miguel Henry, 2019. "PENTROPY: GAUSS module to compute Permutation Entropy point estimates of a time series," Statistical Software Components G00016, Boston College Department of Economics.
    • Handle: RePEc:boc:bocode:g00016
    as

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    File URL: http://fmwww.bc.edu/repec/bocode/p/pentropy_single_final.gss
    File Function: program code
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    Cited by:

    1. Miguel Henry & George Judge, 2019. "Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series," Econometrics, MDPI, vol. 7(1), pages 1-16, March.

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