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2022 | OriginalPaper | Chapter

4. Fisher-Shannon Analysis

Author : Fabian Guignard

Published in: On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory

Publisher: Springer International Publishing

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Abstract

This chapter discusses the Fisher-Shannon analysis, an effective and computationally efficient data exploration tool based on IT quantities. This method is highly versatile and can be used in various situations such as time series discrimination, complexity quantification, detection of non-linear relationships, generation of time series features, detection of dynamical changes and non-stationarity tracking of a signal.

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previous chapter Advanced Exploratory Data Analysis

next chapter Spatio-Temporal Prediction with Machine Learning

In this subsection, all physical interpretations were provided by Dr. Dasaraden Mauree and Dr. Luciano Telesca.

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Title: Fisher-Shannon Analysis
Author: Fabian Guignard
Publisher: Springer International Publishing
Book: On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
Print ISBN: 978-3-030-95230-3

Electronic ISBN: 978-3-030-95231-0

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-030-95231-0_4

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