基于多尺度分析的小麦价格预测研究

Forecasting of Wheatprice Based on Multi-scale Analysis

本文基于分解-重构-集成的思想,构建了一个多尺度组合预测模型,选取小麦作为粮食的代表,预测其价格走势。首先,运用集合经验模态分解方法(EEMD)分解价格序列,然后,用灰色关联分析方法对分量序列进行重构,重构为高频、中频、低频和趋势项四个部分,并从不规则因素、季节因素、重大事件和世界经济水平等方面对这四个部分波动特点进行解释,针对不同特点的分量选择不同的方法进行预测,最后对各预测结果用支持向量机集成,并与其他预测模型的预测结果进行比较。实证结果表明,本文构建的多尺度组合模型的预测效果优于灰色预测GM(1,1)、BP神经网络、SVM方法、ARIMA模型等单模型方法和ARIMA-SVM组合模型以及基于EMD和EEMD分解的其他多尺度组合模型。

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 ensernbleempirical 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.