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

Abstract

Abstract Improving the prediction accuracy of agricultural product futures prices is important for investors, agricultural producers, and policymakers. This is to evade risks and enable government departments to formulate appropriate agricultural regulations and policies. This study employs the 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 autoregressive integrated moving average (ARIMA)—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 is then compared with the benchmark individual models: SVM, NN, and ARIMA. Our main interest in this study is on short‐term forecasting, and thus we only consider 1‐day and 3‐day forecast horizons. The results indicate that the prediction performance of the EEMD combined model is better than that of individual models, especially for the 3‐day forecasting horizon. The study also concluded that the machine learning methods outperform the statistical methods in forecasting high‐frequency volatile components. However, there is no obvious difference between individual models in predicting low‐frequency components.