Computer Science ›› 2019, Vol. 46 ›› Issue (7): 217-223.doi: 10.11896/j.issn.1002-137X.2019.07.033

• Artificial Intelligence • Previous Articles     Next Articles

Missing Data Prediction Algorithm Based on Sparse Bayesian Learning in Coevolving Time Series

SONG Xiao-xiang,GUO Yan,LI Ning,YU Dong-ping   

  1. (College of Communications Engineering,Army Engineering University of PLA,Nanjing 210007,China)
  • Received:2018-05-27 Online:2019-07-15 Published:2019-07-15

Abstract: In view of most of the existing algorithms in predicting the missing data in the coevolving time series are only feasible to be applied to the case where only a low ratio of collected data are missing,an efficient missing data prediction method was proposed in this paper.Firstly,the compressive sensing theory is applied to model the missing data prediction problem in the coevolving time series to the problem of multiple sparse vectors recovery.Secondly,the validity of the model is analyzed from two aspects:whether the sparse representation vector is sufficiently sparse and the sensing matrix satisfies the restricted isometry property.Finally,the novel recovery algorithm based on sparse Bayesian lear-ning,which can solve multiple sparse vector recovery problems by learning some support information,is designed for the characteristics of coevolving time series.Simulation results show that the proposed algorithm can effectively predict the missing data in multiple time series simultaneously.

Key words: Coevolving time series, Missing data, Sparse representation vector, Sensing matrix, Sparse bayesian learning

CLC Number: 

  • TN911.7
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