摘要
最优模型子集的确定和集成权重的设置是组合预测中的两个重要问题,直接关系到组合模型的预测表现。为此,本文提出了一个基于状态转移回归的动态集成时序预测方法,首先基通过计算单体模型和原始数据之间的互信息确定最优模型子集;其次,通过状态转移回归实现单体模型的动态集成,并获得最终预测值。通过对9个国家的主权信用违约互换利差数据进行预测实验,本文发现所提出的状态转移组合预测模型表现良好,不仅优于一般单体预测模型和组合预测模型,还优于基于滑动窗口技术的动态组合预测模型。
The determination of optimal individual model sets and the setting of ensemble weights are two criti‐cal problems for ensemble forecasting,which are related to the prediction performance of the ensemble model.On the one hand,the prediction performance of individual model is unstable and the static ensemble prediction model cannot fully exploit the prediction advantages of individual models;on the other hand,ergodic method to determine the optimal model subset is faced with high computational complexity.To this end,a dynamic ensemble forecasting model is proposed with a regime-switching regression method.First,the optimal individual model set is determined by calculating the mutual information between the individual forecasts and the original data;second,the regime-switching regression is used to ensemble the individual forecasts and get the final prediction values.Through the prediction experiments on the sovereign credit default swaps in nine sample countries,it is found that the proposed regime-switching regression ensemble model performs well,not only better than the individual and combination prediction models but also better than the sliding window technology dynamic combination forecasting model.
作者
冯倩倩
孙晓蕾
郝俊
Feng Qianqian;Sun Xiaolei;Hao Jun(School of Management,Shandong University,Jinan 250100,China;Institutes of Science and Development,Chinese Academy of Sciences,Beijing 100190,China;School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China)
出处
《中国管理科学》
CSSCI
CSCD
北大核心
2024年第2期307-314,共8页
Chinese Journal of Management Science
基金
国家自然科学基金项目(72071197,72201265)
中国博士后科学基金项目(2023T160635,2022M723105)。
关键词
组合预测
动态集成
状态转移回归
互信息
combination forecast
dynamic ensemble
regime-switching
mutual information
