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A hybrid VMD-BiGRU model for rubber futures time series forecasting
Zhu, Qing1,2,3; Zhang, Fan1; Liu, Shan2; Wu, Yiqiong1; Wang, Lin4
2019-11-01
Source PublicationAPPLIED SOFT COMPUTING
ISSN1568-4946
Volume84Pages:13
AbstractAs one of the four major industrial raw materials in the world, natural rubber is closely related to the national economy and people's livelihood. The analysis of natural rubber price and volatility can give hedging guidance to manufacturers and provide investors with uncertainty and risk information to reduce investment losses. To effectively analyses and forecast the natural rubber's price and volatility, this paper constructed a hybrid model that integrated the bidirectional gated recurrent unit and variational mode decomposition for short-term prediction of the natural rubber futures on the Shanghai Futures Exchange. In data preprocessing period, time series is decomposed by variational mode decomposition to capture the tendency and mutability information. The bidirectional gated recurrent unit is introduced to return the one-day-ahead prediction of the closing price and 7-day volatility for the natural rubber futures. The experimental results demonstrated that: (a) variational mode decomposition is an effective method for time series analysis, which can capture the information closely related to the market fluctuations; (b) the bidirectional neural network structure can significantly improve the model performance both in terms of fitting performance and the trend prediction; (c) a correspondence was found between the predicted target, i.e., the price and volatility, and the intrinsic modes, which manifested as the impact of the long-term and short-term characteristics on the targets at different time-scales. With a change in the time scale of forecasting targets, it was found that there was some variation in matching degree between the forecasting target and the mode sub-sequences. (C) 2019 Elsevier B.V. All rights reserved.
KeywordBiGRU Rubber futures Time series VMD Volatility prediction
DOI10.1016/j.asoc.2019.105739
Language英语
Funding ProjectNational Natural Science Foundation (NSFC) Programs of China[71722014] ; National Natural Science Foundation (NSFC) Programs of China[71471141] ; Youth Innovation Team of Shaanxi Universities "Big data and Business Intelligent Innovation Team"
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000490753200049
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/35931
Collection中国科学院数学与系统科学研究院
Corresponding AuthorLiu, Shan
Affiliation1.Shaanxi Normal Univ, Int Business Sch, Xian 710000, Shaanxi, Peoples R China
2.Xi An Jiao Tong Univ, Sch Management, Xian 710049, Shaanxi, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
4.Huazhong Univ Sci & Technol, Sch Management, Wuhan 430074, Hubei, Peoples R China
Recommended Citation
GB/T 7714
Zhu, Qing,Zhang, Fan,Liu, Shan,et al. A hybrid VMD-BiGRU model for rubber futures time series forecasting[J]. APPLIED SOFT COMPUTING,2019,84:13.
APA Zhu, Qing,Zhang, Fan,Liu, Shan,Wu, Yiqiong,&Wang, Lin.(2019).A hybrid VMD-BiGRU model for rubber futures time series forecasting.APPLIED SOFT COMPUTING,84,13.
MLA Zhu, Qing,et al."A hybrid VMD-BiGRU model for rubber futures time series forecasting".APPLIED SOFT COMPUTING 84(2019):13.
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