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学术报告

Bayesian forecast combination using time-varying features

李丰 副教授

（中央财经大学）

报告时间： 2023年10月20日 (星期五) 下午2：00-3：00

报告地点：沙河主E706

报告摘要：In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time-varying features. Our framework estimates weights in the forecast combination via Bayesian log predictive scores, in which the optimal forecast combination is determined by time series features from historical information. In particular, we use an automatic Bayesian variable selection method to identify the importance of different features. To this end, our approach has better interpretability compared to other black-box forecasting combination schemes. We apply our framework to stock market data and M3 competition data. Based on our structure, a simple maximum-a-posteriori scheme outperforms benchmark methods, and Bayesian variable selection can further enhance the accuracy for both point forecasts and density forecasts.

报告人简介：

李丰副教授现任职于中央财经大学统计与数学学院，博士毕业于瑞典斯德哥尔摩大学，研究领域包括贝叶斯统计学，预测方法，大数据分布式学习等，曾获瑞典皇家统计学会 Cramér 奖，第二届全国高校经管类实验教学案例大赛二等奖，现主持一项国家社科基金项目，主要研究成果发表在EJOR，JBES，IJF，JCGS等高水平学术期刊。

邀请人： 罗雪