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

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; in revised form: 8 September 2012 / Accepted: 21 September 2012 / Published: 28 September 2012
PDF Full-text Download PDF Full-Text [698 KB, uploaded 8 October 2012 16:00 CEST]
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

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

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.

AMA Style

Kowalski AM, Martin MT, Plastino A, Judge G. On Extracting Probability Distribution Information from Time Series. Entropy. 2012; 14(10):1829-1841.

Chicago/Turabian Style

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.

Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert