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Artificial bee colony-based combination approach to forecasting agricultural commodity prices
Wang, Jue; Wang, Zhen1; Li, Xiang; Zhou, Hao
2022
Source PublicationINTERNATIONAL JOURNAL OF FORECASTING
ISSN0169-2070
Volume38Issue:1Pages:21-34
AbstractThe fluctuation of agricultural commodity prices has attracted a considerable amount of attention. However, the complexity of the agricultural futures market and the variability of influencing factors makes the prediction of agricultural commodity futures prices difficult. We address the nonlinear characteristics of agricultural commodity futures price series by proposing a forecast combination approach based on a global optimization method, called the Artificial Bee Colony Algorithm (ABC), for forecasting soybean and corn futures prices. Firstly, we used three denoising techniques, namely singular spectral analysis (SSA), empirical mode decomposition (EMD), and variational mode decomposition (VMD), to filter the external noise in the original price series. Then, we generated diverse forecasting sub-models by combining denoising techniques and five popular forecasting models: autoregressive integrated moving average regression (ARIMA), support vector regression (SVR), recurrent neural network (RNN), gated recurrent neural network (GRU), and long-short term memory neural network (LSTM). Finally, we present an ABC approach for three forecast combinations: heterogeneous, semiheterogeneous, and homogeneous combination. Experimental results indicate that the semi-heterogeneous forecast combination is superior to other combination strategies. For corn and soybean prices, ABC-based semi-heterogeneous forecast combinations have error reductions of 53.3% and 50.0% of MAPE and improvements of 32.4% and 34.5% in Dstat compared to the best single models, respectively. (c) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
KeywordAgricultural commodity price Forecast combination Semi-heterogeneous combination Artificial bee colony algorithm Denoising technique
DOI10.1016/j.ijforecast.2019.08.006
Indexed BySCI
Language英语
WOS Research AreaBusiness & Economics
WOS SubjectEconomics ; Management
WOS IDWOS:000731303000003
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/59707
Collection中国科学院数学与系统科学研究院
Corresponding AuthorWang, Zhen
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, CFS, MADIS, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Jue,Wang, Zhen,Li, Xiang,et al. Artificial bee colony-based combination approach to forecasting agricultural commodity prices[J]. INTERNATIONAL JOURNAL OF FORECASTING,2022,38(1):21-34.
APA Wang, Jue,Wang, Zhen,Li, Xiang,&Zhou, Hao.(2022).Artificial bee colony-based combination approach to forecasting agricultural commodity prices.INTERNATIONAL JOURNAL OF FORECASTING,38(1),21-34.
MLA Wang, Jue,et al."Artificial bee colony-based combination approach to forecasting agricultural commodity prices".INTERNATIONAL JOURNAL OF FORECASTING 38.1(2022):21-34.
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