Abstract: Forecasting of grain price is an important area of grain market research.In this paper a new multi-scale combined forecasting model was built based on the idea of decomposition-reconstruction-integration.It selected wheat as representative of grain and forecasted its price trend.It used ensembleempirical mode decomposition (EEMD) to decompose price series, then reconstructed the component sequences into high frequency, middle frequency, low frequency and trend sequences with grey correlation method, which can be explained from the angle of irregular factors, seasonal factor, major events and long-term trend.It forecasted different sequences by different methods according to their characteristics, such as BP neural network,Support Vector Machine (SVM), ARIMA and so on.Finally, it integrated prediction results with SVM.The empirical results show that comparing with GM (1, 1), BP neural network, SVM and other single models, ARIMA-SVM combined model as well as other multi-scale model based on EMD or EEMD, multi-scale combined model obtains the best forecast result.

Key words: multi-scale model, EEMD, grey correlation degree, neural network, SVM