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Entropy 2012, 14(10), 1829-1841; doi:10.3390/e14101829

On Extracting Probability Distribution Information from Time Series

1
CIC Buenos Aires, C. C. 67, 1900 La Plata, Argentina
2
Instituto de F ísica La Plata–CCT-CONICET, C.C. 727, 1900 La Plata, Argentina
3
IFISC (CSIC-UIB), Campues Universitat Illes Balears, E-07122 Palma de Mallorca, Spain
4
207 Giannini Hall, University of California, Berkeley, Berkeley, CA 94720, USA
Member of the Giannini Foundation.
*
Author to whom correspondence should be addressed.
Received: 15 August 2012 / Revised: 8 September 2012 / Accepted: 21 September 2012 / Published: 28 September 2012
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Abstract

Time-series (TS) are employed in a variety of academic disciplines. In this paper we focus on extracting probability density functions (PDFs) from TS to gain an insight into the underlying dynamic processes. On discussing this “extraction” problem, we consider two popular approaches that we identify as histograms and Bandt–Pompe. We use an information-theoretic method to objectively compare the information content of the concomitant PDFs.
Keywords: Bandt–Pompe; histograms; time-series Bandt–Pompe; histograms; time-series
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Kowalski, A.M.; Martin, M.T.; Plastino, A.; Judge, G. On Extracting Probability Distribution Information from Time Series. Entropy 2012, 14, 1829-1841.

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