2022/10/14 by John Stewart Fabila-Carrasco, Chao Tan, Fabila-Carrasco, John Stewart +3
Computer Science · Economics, Econometrics and Finance · #05C76 #76T10 #94A17: 05C21 #Combinatorics (math.CO) #Complex Systems and Time Series Analysis #Data Analysis #FOS: Mathematics #FOS: Physical sciences #Neural Networks and Applications #Statistics and Probability (physics.data-an) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2210.09030
openalex publication_date 2022/10/14 · openalex created_date 2022/10/20 · openalex updated_date 2026/07/28
Using a graph-based approach, we propose a multiscale permutation entropy to explore the complexity of multivariate time series over multiple time scales. This multivariate multiscale permutation entropy (MPEG) incorporates the interaction between channels by constructing an underlying graph for each coarse-grained time series and then applying the recent permutation entropy for graph signals. Given the challenge posed by noise in real-world data analysis, we investigate the robustness to noise of MPEG using synthetic time series and demonstrating better performance than similar multivariate entropy metrics. Two-phase flow data is an important industrial process characterised by complex, dynamic behaviour. MPEG characterises the flow behaviour transition of two-phase flow by incorporating information from different scales. The experimental results show that MPEG is sensitive to the dynamic of flow patterns, allowing us to distinguish between different flow patterns.