中央财经大学李丰副教授：基于狄利克雷过程的无限预测组合

报告摘要
Forecast combination integrates information from various sources by consolidating multiple forecast results from the target time series. Instead of the need to select a single optimal forecasting model, this paper introduces a deep learning ensemble forecasting model based on the Dirichlet process. Initially, the learning rate is sampled with three basis distributions as hyperparameters to convert the infinite mixture into a finite one. All checkpoints are collected to establish a deep learning sub-model pool, and weight adjustment and diversity strategies are developed during the combination process. The main advantage of this method is its ability to generate the required base learners through a single training process, utilizing the decaying strategy to tackle the challenge posed by the stochastic nature of gradient descent in determining the optimal learning rate. To ensure the method's generalizability and competitiveness, this paper conducts an empirical analysis using the weekly dataset from the M4 competition and explores sensitivity to the number of models to be combined. The results demonstrate that the ensemble model proposed offers substantial improvements in prediction accuracy and stability compared to a single benchmark model.

嘉宾简介
李丰博士任职于中央财经大学统计与数学学院，副教授，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。李丰博士最新研究成果发表在统计期刊JCGS, 管理期刊EJOR (ABS4)，会计期刊CAR (FT50)等。他同时著有 Bayesian Modeling of Conditional Densities，《大数据分布式计算与案例》和《统计计算》。

直播分享时间：2024年4月12日