On Extracting Probability Distribution Information from Time Series
Andres Mauricio Kowalski, María T. Martín, Angelo Plastino, George G. Judge · Entropy · 2012
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.