Improving the prediction accuracy of agricultural product futures prices is important for the investors, agricultural producers and policy makers. This is to evade the risks and enable the government departments to formulate appropriate agricultural regulations and policies. This study employs Ensemble Empirical Mode Decomposition (EEMD) technique to decompose six different categories of agricultural futures prices. Subsequently three models, Support Vector Machine (SVM), Neural Network (NN) and ARIMA models are used to predict the decomposition components. The final hybrid model is then constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model were then compared with the benchmark individual models, SVM, NN, and ARIMA. Our main interest in this study is on the short‐term forecasting, and thus we only consider 1‐day and 3‐days forecast horizons. The results indicated that the prediction performance of EEMD combined model is better than that of individual models, especially for the 3‐days forecasting horizon. The study also concluded that the machine learning methods outperform the statistical methods to forecast high‐frequency volatile components. However, there is no obvious difference between individual models in predicting the low‐frequency components.

中文翻译：

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基于集合经验模式分解的农产品期货价格最优预测组合

提高农产品期货价格的预测准确性，对于投资者、农业生产者和决策者都具有重要意义。这是为了规避风险，使政府部门能够制定相应的农业法规和政策。本研究采用集成经验模式分解 (EEMD) 技术分解六种不同类别的农产品期货价格。随后使用支持向量机 (SVM)、神经网络 (NN) 和 ARIMA 模型这三个模型来预测分解分量。然后通过比较分解组件的预测性能来构建最终的混合模型。然后将组合模型的预测性能与基准单个模型、SVM、NN 和 ARIMA 进行比较。我们对这项研究的主要兴趣是短期预测，因此我们只考虑 1 天和 3 天的预测范围。结果表明，EEMD 组合模型的预测性能优于单个模型，尤其是对于 3 天的预测范围。该研究还得出结论，机器学习方法在预测高频挥发性成分方面优于统计方法。然而，各个模型在预测低频分量方面没有明显差异。该研究还得出结论，机器学习方法在预测高频挥发性成分方面优于统计方法。然而，各个模型在预测低频分量方面没有明显差异。该研究还得出结论，机器学习方法在预测高频挥发性成分方面优于统计方法。然而，各个模型在预测低频分量方面没有明显差异。