Prediction of time series based on Huang transform and BP neural network

Huang transform is a new method for non-stationary signal analysis developed by Norden E.Huang et al in 1998.This paper studies the application of Huang transform to time series.Firstly,the time series are decomposed into a finite and often small number of Intrinsic Mode Functions(IMF) and one Remnant Function(RF).IMF components can reflect every scaling character and RF components can represent the total trend of the origin time series.Secondly,BP neural network is applied to predict IMF and RF.Experiment results illustrate that the new predicting method is better than wavelet analysis with BP neural network and it improves the forecasting accuracy.