On the Automatic Parameter Selection for Permutation Entropy

Abstract

Permutation Entropy (PE) has been shown to be a useful tool for time series analysis due to its low computational cost and noise robustness. This has drawn for its successful application in many fields. Some of these include damage detection, disease forecasting, and financial volatility analysis. However, to successfully use PE, an accurate selection of two parameters is needed: the permutation dimension $n$ and embedding delay $\tau$. These parameters are often suggested by experts based on a heuristic or by a trial and error approach. unfortunately, both of these methods can be time-consuming and lead to inaccurate results. To help combat this issue, in this paper we investigate multiple schemes for automatically selecting these parameters with only the corresponding time series as the input. Specifically, we develop a frequency-domain approach based on the least median of squares and the Fourier spectrum, as well as extend two existing methods: Permutation Auto-Mutual Information (PAMI) and Multi-scale Permutation Entropy (MPE) for determining $\tau$. We then compare our methods as well as current methods in the literature for obtaining both $\tau$ and $n$ against expert-suggested values in published works. We show that the success of any method in automatically generating the correct PE parameters depends on the category of the studied system. Specifically, for the delay parameter $\tau$, we show that our frequency approach provides accurate suggestions for periodic systems, nonlinear difference equations, and ECG/EEG data, while the mutual information function computed using adaptive partitions provides the most accurate results for chaotic differential equations. For the permutation dimension $n$, both False Nearest Neighbors and MPE provide accurate values for $n$ for most of the systems with $n=5$ being suitable in most cases.

arXiv commentAbstract with all example systems provided in appendix

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• Dynamic State Analysis of a Driven Magnetic Pendulum using Ordinal Partition Networks and Topological Data Analysis
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