Symbolic Entropy Analysis and Its Applications III

Dear Colleagues,

Symbolic data analysis has received a great deal of attention over the last few years and has been applied to many research areas, including astrophysics and geophysics, biology and medicine, fluid flow, chemistry, mechanical systems, artificial intelligence, communication systems, and, recently, data mining and big data. A fundamental step in this methodology is the quantization of original data into a corresponding sequence of symbols. The resulting time series is then considered a transformed version of the original data, allowing to highlight its temporal information. Indeed, it has been proven that this symbolization procedure can notably improve signal-to-noise ratios in some noisy time series. Moreover, symbolic data analysis also makes communication and numerical computation more efficient and effective, compared with the processing of continuous-valued time series.

However, symbolization of a time series always involves information loss; hence, this process deserves special attention. This challenge, along with other problems associated with symbolic entropy analysis, has been addressed in the first and second volume of the Special Issue on “Symbolic Entropy Analysis and Its Applications”. Given its success, this third volume aims to compile key current research on novel symbolization approaches, as well as applications of this kind of analysis to reveal pioneering information from different types of time series. Hence, manuscripts dealing with these topics will be welcome.

Volume I:　https://www.mdpi.com/journal/entropy/special_issues/symbolic_entropy_analysis

Volume II:　https://www.mdpi.com/journal/entropy/special_issues/Symbolic_Entropy_II

Prof. Dr. Raúl Alcaraz
Guest Editor

Manuscript Submission Information

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