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

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  • Yongmei Fang
  • Bo Guan
  • Shangjuan Wu
  • Saeed Heravi
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.

Suggested Citation

  • Yongmei Fang & Bo Guan & Shangjuan Wu & Saeed Heravi, 2020. "Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 877-886, September.
    • Handle: RePEc:wly:jforec:v:39:y:2020:i:6:p:877-886
    DOI: 10.1002/for.2665
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    7. Juan D. Borrero & Jesus Mariscal, 2022. "Predicting Time SeriesUsing an Automatic New Algorithm of the Kalman Filter," Mathematics, MDPI, vol. 10(16), pages 1-13, August.
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    9. Ramos & Pablo Negri & Martín Breitkopf & María Laura Ojeda, 2021. "From International to Regional Commodity Price Pass-through Using Self-Driven Recurrent Networks," Asociación Argentina de Economía Política: Working Papers 4513, Asociación Argentina de Economía Política.
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