基于spike-and-slab先验的贝叶斯时间序列模型  

Bayesian Time-series Model Based on spike-and-slab Prior

作　　者：郭晨蕾 李东喜 GUO Chenlei;LI Dongxi(College of Mathematics,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China;College of Data Science,Taiyuan University of Technology,Taiyuan 030024,China)

机构地区：[1]太原理工大学数学学院,山西晋中030600 [2]太原理工大学大数据学院,太原030024

出　　处：《计算机科学》2023年第S02期644-649,共6页Computer Science

摘　　要：贝叶斯方法通过引入先验信息并结合似然的方法进行参数估计和变量选择,使模型估计和预测结果更为精确。在贝叶斯框架下考虑时间序列之间的相关性,将偏自相关系数融合先验信息,提出基于spike-and-slab先验的贝叶斯层次时间序列模型(Spike-and-slab Prior with Partial Autocorrelation Coefficients,SS-PAC)。SS-PAC模型采用spike-and-slab先验并结合偏自相关系数,实现时间序列滞后阶数的选择、参数估计和预测。基于模拟数据和真实数据的实证研究表明,该模型相较于以往模型在变量选择和预测结果上表现更优。Bayesian method makes the results of estimation and prediction more accurate by introducing prior information and combining with likelihood for parameter estimation and variable selection.ABayesian hierarchical time-series model based on spike-and-slab prior with partial autocorrelation coefficients(SS-PAC)is proposed under the Bayesian framework,considering the correlation between time series,fusing with the partial autocorrelation coefficient and prior information,the SS-PAC model uses spike-and-slab prior and partial autocorrelation coefficient to realize the selection,parameter estimation and prediction of time series lag order.Empirical research through simulated data and real data shows that the model performs better than previous models in variable selection and prediction results.

关 键 词：时间序列预测 spike-and-slab先验 贝叶斯方法 偏自相关系数 变量选择 

分 类 号：O212.8[理学—概率论与数理统计]