Abstract: In order to solve the problem of low construction efficiency and low accuracy of the nonlinear time series prediction model in complex systems, a Hurst-EMD prediction algorithm based on combination model is proposed. This algorithm uses EMD algorithm to decompose the nonlinear time series into individual IMFs representing the characteristics of the original series, and then introduces Hurst exponent to integrate the similar IMF into new components. Finally, LS-SVR and ARIMA models are used for combinational prediction. In this algorithm, the process of sequence classification and integration is designed, the number of calculation is optimized, and an efficient and accurate prediction model is constructed. In order to verify the validity of the model, the public data set of Shanghai stock index and real traffic flow data are used for testing. The experimental results show that the improved HURST-EMD combination model has better prediction accuracy while improving the prediction efficiency.

Key words: nonlinear time series, empirical model decomposition, Hurst exponent, combinational prediction model