Assessing the Preprocessing Benefits of Data-Driven Decomposition Methods for Phase Permutation Entropy—Application to Econometric Time Series - Erwan Pierron; Meryem Jabloun (Engineering Proceedings)
This paper investigates the efficacy of various data-driven decomposition methods combined with Phase Permutation Entropy (PPE) to form a promising complexity metric for analyzing time series. PPE is a variant of classical permutation entropy (PE), while the examined data-driven decomposition methods include Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Empirical Wavelet Transform (EWT), Seasonal and Trend decomposition using Loess (STL), and Singular Spectrum Analysis-based decomposition (SSA). To our knowledge, this combination has not been explored yet. Our primary aim is to assess how these preprocessing methods affect PPE’s ability to capture temporal structural complexities within time series. This evaluation encompasses the analysis of both simulated and econometric time series. Our results reveal that combining SSA with PPE produces superior advantages for measuring the complexity of seasonal time series. Conversely, VMD combined with PPE proves to be the less advantageous strategy. Overall, our study illustrates that combining data-driven preprocessing methods with PPE offers greater benefits compared to combining them with traditional PE in quantifying time series complexity.