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.
Highlights
Time series (TS) data originating from different physical/natural systems/processes usually contain extremely valuable information
We present our main results regarding the two systems under scrutiny here
The histogram results greatly differ from the “uniform-probabilistic distribution functions (PDFs)”, which suggests that its information-content is significant
Summary
Time series (TS) data originating from different physical/natural systems/processes usually contain extremely valuable information. Such information is conveyed in the form of probabilistic distribution functions (PDFs) that, in some sense, represent the TS. The problem we discuss is how to best extract that information from the time series. This, is tantamount to asking for the best. The indirect data always possess a stochastic component due to the omnipresent dynamical noise [1,2]. The specific procedure one employs in extracting a TS from given data seriously affects the quality of the information one may gain
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