摘要
原油价格受国际政治、经济、军事、外交以及其他复杂因素的影响,这些因素的频繁变化使油价表现出随机波动,给原油投资及交易决策带来困难,准确预测油价已成为能源领域学术界的研究热点.但是,现有关于原油价格预测的文献大多数是预测原油价格的数值而不是变化方向,而且不是同时预测原油价格和波动率,因此无法给投资者充分的决策指导信息.为了填补这一研究空白,提出一种结合转移网络(TN)、链接预测(LP)、长短期记忆模型(LSTM)和支持向量机(SVM)的新的混合模型TN-LP-LSTM-SVM来更精确地预测WTI期货次日价格变化方向和波动率大小,为投资者、能源相关企业和参与政策决策的政府人员提供有益的建议.在不同的时间窗口下(h∈[1, 50]且h∈Z~+)对TN-LP-LSTM-SVM与CNN-SVM、LSTM和SVM的预测精度作比较,发现在进行中长期预测时(h≥4), TN-LP-LSTM-SVM总是稳健地优于CNN-SVM、LSTM和SVM.
Crude oil prices are influenced by international political, economic, military, diplomatic and other complex factors, and the frequent changes in these factors cause oil prices to exhibit random fluctuations, making crude oil investment and trading decisions difficult. Therefore, predicting oil prices accurately has become a hot research topic in the academic field of energy. However, most of the existing literature on the crude oil price forecasting predicts the value of crude oil prices rather than the change direction, and does not predict crude oil prices and volatility simultaneously,thus can’t give investors sufficient information to guide their decisions. To fill this research gap, this paper proposes a new hybrid TN-LP-LSTM-SVM model combining the transition network(TN), link prediction(LP), long short-term memory model(LSTM) and support vector machine(SVM) to predict the next-day price change direction and volatility size of WTI futures more accurately, providing useful advices for investors, energy-related companies, and government personnel involved in policy decisions. Comparing the prediction accuracy of the TN-LP-LSTM-SVM model with the CNN-SVM model, LSTM and SVM for different time windows(h ∈ [1, 50] and h ∈ Z), we find that the TN-LP-LSTM-SVM model always outperforms the CNN-SVM model, LSTM and SVM robustly for medium and long term predictions(h ≥ 4).
作者
赵戈雅
薛明皋
ZHAO Ge-ya;XUE Ming-gao(School of Management,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《控制与决策》
EI
CSCD
北大核心
2022年第10期2627-2636,共10页
Control and Decision
基金
国家自然科学基金面上项目(70871046,71171091,71471070)。
关键词
原油期货
方向预测
转移网络
链接预测
长短期记忆模型
支持向量机
crude oil futures
direction forecast
transition network
link prediction
long short-term memory model
support vector machine
