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【信息通知】魏云捷博士学术讲座

2019-9-18 16:21:21

 

为了进一步营造科学研究范围,开拓国际学术视野,提升研究能力、学术水平和国际交流能力,大连海事大学航运经济与管理学院、综合交通运输协同创新中心于2019年920日邀请了中国科学院预测科学研究中心助理研究员魏云捷博士来我校做学术讲座。      

image.png讲座嘉宾:魏云捷

讲座地点:远望楼4楼7号报告厅

讲座时间:2019年9月20日15:30-17:30

报告主题:Interval Decomposition Ensemble Approach for Crude Oil Price                                  Forecasting/基于区间分解集成的原油预测方法

报告人简介:中国科学院数学与系统科学研究员助理研究员。2017年获中国科学院大学博士学位,2018年获香港城市大学博士学位。研究兴趣包括:经济预测理论与方法和经济政策分析等。在重要学术期刊上发表(含接受发表)论文22篇,其中SCI/SSCI收录9篇,EI收录5篇,国家自然科学基金委员会管理类重要期刊A级/CSSCI收录6篇,包括Applied Energy、IEEE Transactions on Systems, Man and Cybernetics: Systems、Atmospheric Pollution Research、Energy Economics、Economic Modelling、Tourism Management等SCI/SSCI期刊。在汇率和进出口分析与预测方面作为主笔完成了政策研究报告23篇,其中5篇得到李克强总理、汪洋副总理和马凯副总理的重要批示,部分政策建议被商务部、央行及国家外汇管理局所采纳,有效地支持了国家高层决策。

摘要Crude oil is one of the most important energy sources in the world, and it is very important for policymakers, enterprises and investors to forecast the price of crude oil accurately. This study proposes an interval decomposition ensemble (IDE) learning approach to forecast interval-valued crude oil price by integrating bivariate empirical mode decomposition (BEMD), interval MLP (MLPI) and interval exponential smoothing method (HoltI). Firstly, the original interval-valued crude oil price is transformed into a complex-valued signal. Secondly, BEMD is used to decompose the constructed complex-valued signal into a finite number of complex-valued intrinsic mode functions (IMFs) components and one complex-valued residual component. Thirdly, MLPI is used to simultaneously forecast the lower and the upper bounds of each IMF (non-linear patterns), and HoltI is used for modeling the residual component (linear pattern). Finally, the forecasting results of the lower and upper bounds of all the components are combined to generate the aggregated interval-valued output by employing another MLPI as the ensemble tool. The empirical results show that our proposed IDE learning approach with different forecasting horizons and different data frequencies significantly outperforms some other benchmark models by means of forecasting accuracy and hypothesis tests.