Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 221200131-6.doi: 10.11896/jsjkx.221200131

• Big Data & Data Science • Previous Articles     Next Articles

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

GUO Chenlei1, LI Dongxi2   

  1. 1 College of Mathematics,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
    2 College of Data Science,Taiyuan University of Technology,Taiyuan 030024,China
  • Published:2023-11-09
  • About author:GUO Chenlei,born in 1997,postgra-duate.Her main research interests include variable selection and so on.
    LI Dongxi,born in 1982,Ph.D,associate professor,postgraduate supervisor.His main research interests include high dimensional data analysis,data mining,machine learning,biostatistics and biological mathematics.

Abstract: 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.

Key words: Time-series prediction, Spike-and-slab prior, Bayesian method, Partial autocorrelation coefficient, Variable selection

CLC Number: 

  • O212.8
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