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Algorithms 2017, 10(3), 108; https://doi.org/10.3390/a10030108

Performance Analysis of Four Decomposition-Ensemble Models for One-Day-Ahead Agricultural Commodity Futures Price Forecasting

1
School of Economics and Management, China University of Geosciences, Wuhan 430074, China
2
Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 17 July 2017 / Revised: 31 August 2017 / Accepted: 9 September 2017 / Published: 12 September 2017
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Abstract

Agricultural commodity futures prices play a significant role in the change tendency of these spot prices and the supply–demand relationship of global agricultural product markets. Due to the nonlinear and nonstationary nature of this kind of time series data, it is inevitable for price forecasting research to take this nature into consideration. Therefore, we aim to enrich the existing research literature and offer a new way of thinking about forecasting agricultural commodity futures prices, so that four hybrid models are proposed based on the back propagation neural network (BPNN) optimized by the particle swarm optimization (PSO) algorithm and four decomposition methods: empirical mode decomposition (EMD), wavelet packet transform (WPT), intrinsic time-scale decomposition (ITD) and variational mode decomposition (VMD). In order to verify the applicability and validity of these hybrid models, we select three futures prices of wheat, corn and soybean to conduct the experiment. The experimental results show that (1) all the hybrid models combined with decomposition technique have a better performance than the single PSO–BPNN model; (2) VMD contributes the most in improving the forecasting ability of the PSO–BPNN model, while WPT ranks second; (3) ITD performs better than EMD in both cases of corn and soybean; and (4) the proposed models perform well in the forecasting of agricultural commodity futures prices. View Full-Text
Keywords: agricultural commodity futures prices; back propagation neural network (BPNN); particle swarm optimization (PSO); decomposition methods agricultural commodity futures prices; back propagation neural network (BPNN); particle swarm optimization (PSO); decomposition methods
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wang, D.; Yue, C.; Wei, S.; Lv, J. Performance Analysis of Four Decomposition-Ensemble Models for One-Day-Ahead Agricultural Commodity Futures Price Forecasting. Algorithms 2017, 10, 108.

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