A Complex Empirical Mode Decomposition for Multivariant Traffic Time Series

Data-driven modeling methods have been widely used in many applications or studies of traffic systems with complexity and chaos. The empirical mode decomposition (EMD) family provides a lightweight analytical method for non-stationary non-linear data. However, large amount data practice are usually multidimensional, so the EMD cannot be directly those In this paper, to calculate extremum point envelope-like function (series) from complex is proposed that can applied two-variate time-series Compared existing multivariate EMD, has advantages computational burden, flexibility adaptivity. Two-dimensional trajectory were test its oscillatory characteristics extracted. decomposed feature data-driven analysis modeling. also extends utilization such as denoising, pattern recognition, flow dynamic evaluation, prediction, etc.