Artificial bee colony-based combination approach to forecasting agricultural commodity prices

doi:10.1016/j.ijforecast.2019.08.006

	Artificial bee colony-based combination approach to forecasting agricultural commodity prices
	Wang, Jue ; Wang, Zhen 2; Li, Xiang ; Zhou, Hao
刊名	INTERNATIONAL JOURNAL OF FORECASTING
	2022
卷号	38 期号:1 页码:21-34
关键词	Agricultural commodity price Forecast combination Semi-heterogeneous combination Artificial bee colony algorithm Denoising technique
ISSN号	0169-2070
DOI	10.1016/j.ijforecast.2019.08.006
英文摘要	The 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.
WOS研究方向	Business & Economics
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000731303000003
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/59706]
专题	中国科学院数学与系统科学研究院
通讯作者	Wang, Zhen
作者单位	1.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, CFS, MADIS, Beijing 100190, Peoples R China
推荐引用方式 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.