Abstract:

In order to improve the prediction accuracy of non-stationary time series, the paper used Empirical Mode Decomposition(EMD) method of Hilbert-Huang transform

theory to decompose non-stationary time series into several IMF components of single frequency. Using the neural network model to predict IMF, the prediction results are

reconstructed and weighted. The accuracy of prediction will be improved. It can also predict the transport volume in a certain period of time on the basis of the historical

passenger data. The experimental results show that the improved algorithm is better than the neural network method, etc.

Key words: Hilbert-Huang transform； prediction accuracy； Empirical Mode Decomposition(EMO), time series； neural network