Analysis of complex time series based on EMD energy entropy plane

Empirical mode decomposition (EMD) is a self-adaptive signal processing method that can be applied to nonlinear and non-stationary processes perfectly. In view of this good ability of EMD, in this paper, we propose a new methodEMD energy entropy planewhich combines two different toolsEMD energy entropy and complexity-entropy causality planeto analyze time series. Firstly, we apply EMD energy entropy plane to synthetic data, such as logistic map, Henon map, ARFIMA model and so on, finding that the EMD energy entropy plane presents different trends and distributions when the map is in periodic cycles and chaos. Then we demonstrate the application of EMD energy entropy plane in stock markets. Results show that it is an effective tool of distinguishing two kinds of financial markets. In addition, the introduction of multi-scale reveals the variation law of EMD energy entropy plane at different scales.