勷勤数学•专家报告

题      目：Crude oil futures price forecasting based on variational and empirical mode decompositions and transformer model

报  告  人：赖永增  教授  (邀请人：杨舟)

                                 加拿大劳瑞尔大学

时      间：12月17日  10：00-11：00

地     点：数科院西楼二楼会议室

报告人简介:

       赖永增(Yongzeng Lai, ylai@wlu.ca) 是加拿大劳瑞尔大学数学系正教授，他于1983年和1988年分别在中山大学数学系获得学士学位和硕士学位，于2000年1 月在美国加州克莱蒙研究生大学 (The Claremont Graduate University,Claremont, California, USA) 获得博士学位，2000 年5 月至2002 年6 月在加拿大滑铁卢大学高级金融研究中心和统计与精算学系做博士后研究员，2002 年6 月到现在一直在加拿大劳瑞尔大学数学系做教授。

主要研究领域包括金融数学（衍生产品的定价与风险管理、金融计算、 投资组合优化、 随机分析在金融和保险中的应用）、微分方程在金融和经济学中的应用、蒙特卡洛和拟蒙特卡洛仿真方法及应用; 机器学习及其应, 尤其在经济金融中的应用。他在Automatica, Computers & Operations Research, Economic Modeling,Expert Systems with Applications, Energy Economics, Finance Research Letters, Insurance Mathematics and Economics, Journal of Computational Finance, Nature - Humanities and Social Sciences Communications, North American Journal of Finance and Economics, Nonlinear Analysis, Resources Policy等国际期刊已经发表了60 多篇论文。

摘      要：

       Crude oil is a raw, natural, but non-renewable resource. It is one of the world's most important commodities, and its price can have ripple effects through the broader economy. Prediction of crude oil prices plays a crucial role in the investment of crude oil and remains challenging. Due to the deficiencies neglecting residual factors when forecasting using conventional combination models, such as the autoregressive moving average and the long short-term memory for prediction, the variational mode decomposition (VMD)-empirical mode decomposition (EMD)-Transformer model is proposed to predict the crude oil prices in this study. This model integrates a second decomposition and Transformer model-based machine learning method. More specifically, we employ the VMD technique to decompose the original sequence into variational mode filtering (VMF) and a residual sequence, followed by using EMD to decompose the residual sequence. Ultimately, we apply the Transformer model to predict the decomposed modal components and superimpose the results to produce the final forecasted prices. Further empirical test results demonstrate that the proposed quadratic decomposition composite model can comprehensively identify the characteristics of WTI and Brent crude oil futures daily price series. The test results also illustrate that the proposed VMD-EMD-Transformer model outperforms the other three models—long short-term memory (LSTM), Transformer, and VMD-Transformer—in forecasting crude oil prices. Details will be presented.

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