Multi-scale Analysis Of Shanghai Composite Index Time Series Based On Ensemble Empirical Mode Decomposition

Recent years, the method for dealing with the nonlinear and non-stationary data, which is a hot and difficult issue in the field of data analysis. This paper summarized Hilbert-Huang transform theory, elaborated empirical mode decomposition process. By building Ensemble empirical mode decomposition algorithm, in order to effectively address modal mixture phenomenon of data. We use EEMD method to analyze the volatility and periodicity of Shanghai Composite Index (SCI). Firstly, we applied the Ensemble Empirical Mode Decomposition (EEMD) method to analyze Shanghai Composite Index (SCI), which is to decompose the original time series into a series of intrinsic mode functions (IMF) and trend item. Intrinsic mode functions can be analyzed by statistical analysis and fitting of distribution in order to get the characteristics of its distribution. We applied EEMD method to research typical increasing and decreasing situations, which are two typical stages of Shanghai Composite Index. At last, by analyzing the volatility and periodicity of each functions, it suggested the different volatility features of the Shanghai Composite Index with different scales, as well as the fluctuation cycle and features of those periods with typical increasing and decreasing situations. EEMD algorithm is introduced to achieve multi-scale analysis of Shanghai Composite Index time series, this will provide a new and effective analysis methods for the stock data processing. Stock market has a significant impact on macroeconomic. It plays very important roles that studying the variation and fluctuation features of the stock index.