An improved estimator of Shannon entropy with applications to systems with memory

Juan De Gregorio, David Sánchez and Raúl Toral

Chaos, Solitons & Fractals, 2022, vol. 165, issue P1

Abstract: We investigate the memory properties of discrete sequences built upon a finite number of states. We find that the block entropy can reliably determine the memory for systems modeled as Markov chains of arbitrary finite order. Further, we provide an entropy estimator that remarkably gives accurate results when correlations are present. To illustrate our findings, we calculate the memory of daily precipitation series at different locations. Our results are in agreement with existing methods being at the same time valid in the undersampled regime and independent of model selection.

Keywords: Markov processes; Shannon entropy; Data analysis; Systems with memory (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:165:y:2022:i:p1:s0960077922009766

DOI: 10.1016/j.chaos.2022.112797

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