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On Extracting Probability Distribution Information from Time Series
CIC Buenos Aires, C. C. 67, 1900 La Plata, Argentina
Instituto de F ísica La Plata–CCT-CONICET, C.C. 727, 1900 La Plata, Argentina
IFISC (CSIC-UIB), Campues Universitat Illes Balears, E-07122 Palma de Mallorca, Spain
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; in revised form: 8 September 2012 / Accepted: 21 September 2012 / Published: 28 September 2012
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
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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.
Kowalski AM, Martin MT, Plastino A, Judge G. On Extracting Probability Distribution Information from Time Series. Entropy. 2012; 14(10):1829-1841.
Kowalski, Andres M.; Martin, Maria Teresa; Plastino, Angelo; Judge, George. 2012. "On Extracting Probability Distribution Information from Time Series." Entropy 14, no. 10: 1829-1841.