This paper introduces Multivariate Multiscale Graph-based Dispersion Entropy (mvDEG), a computationally efficient method for analyzing multivariate time series data within graph and complex network frameworks. mvDEG combines temporal dynamics with topological relationships, offering advantages over traditional nonlinear entropy methods. Its effectiveness is demonstrated through synthetic and real-world data analysis, including two-phase flow regimes and weather data. The method's linear computational time complexity, achieved through optimized matrix power calculations, makes it suitable for large-scale and real-time applications.
Publisher
IEEE Transactions on Signal Processing
Published On
Jan 01, 2024
Authors
John Stewart Fabila-Carrasco, Chao Tan, Javier Escudero
Tags
Multivariate Analysis
Graph Theory
Dispersion Entropy
Time Series Data
Computational Efficiency
Complex Networks
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