Time Series Forecasting Method Based on Huang Transform and BP Neural Network

This paper studies the application of Huang transform to time series forecasting. Firstly, the time series are decomposed into a finite and often small number of intrinsic mode functions (IMF) and one residual function (RF). IMF components can characterize local properties and RF components can represent the total trend of the origin time series. Secondly, BP neural network is applied to forecast IMF and RF. The experiment results illustrate that the new forecasting method is better than the wavelet analysis with BP neural network and it improves the forecasting accuracy.