Permutation entropy: a natural complexity measure for time series.

By Chstoph Bandt, B. Pompe. Physical Review Letters, 2002

4,296 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b1ab25353d16bf639b1b53768374ee79065b5011

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Citations in the corpus

SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction (2021, methodology): “Predictability estimation: inspired by [16, 30], we use permutation entropy ( PE ) [6] to measure the predictability of the original input and the enhanced representation learnt by SCINet.”
A Review of Radio Frequency Fingerprinting Techniques (2020, methodology): “The MLE of the slope k can be estimated by the least-squares ﬁtting algorithm as in the following equation [54]: According to the above equations, the GLRT detector deﬁnes as follows [40]: The estimated start point of transient signal ˆ n 0 is the maximum of the GLRT detector, deﬁned in equation…”
Complexity–entropy analysis of daily stream flow time series in the continental United States (2014, methodology): “Bandt and Pompe (2002) introduced a simple method to define this probability distribution taking into account the time causality of the process dynamics.”
Ordinal time series analysis (2005, background): “A first step in this direction was done in [3] where we studied the order patterns at n equally spaced time points t, t + d, t + 2d, .”
JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems (2014, background): “• Permutation entropy approaches (Bandt and Pompe, 2002) estimate the relevant PDFs based on the relative ordinal structure of the joint vectors (see Section S.”
Wavelet Multiresolution Complex Network for Analyzing Multivariate Nonlinear Time Series (2017, methodology): “…correspondence 1 Different nonlinear time series analysis methods have been developed to fulfill this challenging task, e.g., chaotic analysis [Daw et al., 1995], permutation entropy [Bandt & Pompe, 2002], recurrence plot [Marwan et al., 2007], and time-frequency representation [Gao et al., 2016].”
Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines. (2015, background): “(2012) and the seminal paper y Bandt and Pompe (2002).”