Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices

TLDR: 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, and the machine learning methods outperform the statistical methods to forecast high‐frequency volatile components.

Abstract: 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.