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
[1] SOWMYA R,SUNEETHA K R.Data mining with big data[C]∥ International Conference on Intelligent Systems and Control.IEEE,2017:246-250.
[2] SHI W,ZHU Y,YU P S,et al.Temporal dynamic matrix factori- zation for missing data prediction in large scale coevolving time series [J].IEEE Access,2017,4(99):6719-6732.
[3] ELENI I,VLAHOGIANNI J,GOLIAS C,et al.Short-term traffic forecasting:Overview of objectives and methods [J].Transport Reviews,2004,24(5):533-557.
[4] BALOUJI E,SALOR Q,ERMIS M.Exponential smoothing of multiple reference frame components with GPUs for real-time detection of time-varying harmonics and inter harmonics of EAF currents[C]∥IEEE Industry Applications Society Meeting.IEEE,2017:1-8.
[5] KOZERA R,WILKOLAZKA M.Natural spline interpolation and exponential parameterization for length estimation of curves[C]∥International Conference of Numerical Analysis & Applied Mathematics.AIP Publishing LLC,2017:1-140.
[6] AL-DEEK H M,CHANDRA C.New algorithms for filtering and imputation of real-time and archived dual-loop detector data in 1-4 data warehouse[C]∥Meeting of the Transportation-Research-Board.2004:116-126.
[7] BOYLES S D.A comparison of interpolation methods for mis- sing traffic volume data[C]∥Transportation Research Board Annual Meeting.2011:23-27.
[8] LIPPI M,BERTINI M,FRASCONI P.Short-term traffic flow forecasting:An experimental comparison of time-series analysis and supervised learning[J].IEEE Transactions on Intelligent Transportation Systems,2013,14(2):872-882.
[9] LI Y,LI Z,LI L,et al.Comparison on PPCA,KPPCA and MPPCA based missing data imputing for traffic flow[C]∥Procee-dings of IEEE Conference on Intelligent Transportation System.2013:1535-1540.
[10] SHI W,ZHU Y,YU P.Effective Prediction of Missing Data on Apache Spark over Multivariable Time Series[J].IEEE Tran-sactions on Big Data,2017,PP(99):1.
[11] STRAUMAN A S,BIANCHI F M,MIKALSEN K.Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks[C]∥IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).2018:307-310.
[12] CAI Y,TONG H,FAN W,et al.Fast mining of a network of coevolving time series[C]∥2015 SIAM International Conference on Data Mining.2015:298-306.
[13] HADI A,WAHIDAH I.Delay estimation using compressive sensing on WSN IEEE 802.15.4[C]∥International Conference on Control,Electronics,Renewable Energy and Communications.IEEE,2017:192-197.
[14] ARJOUNE Y,KAABOUCH N,GHAZI H E,et al.Compressive sensing:Performance comparison of sparse recovery algorithms[C]∥Computing and Communication Workshop and Con-ference.IEEE,2017:1-7.
[15] RHEE I,SHI N M,HONG S,et al.Mobility traces[OL].
[16] SAMUEL M.Intel Lab Data[OL].
[17] FONOLLOSA J,SHEIK S,HUERTA R,et al.Reservoir computing compensates slow response of chemo sensor arrays exposed to fast varying gas concentrations in continuous monitoring[J].Sensors,2015,215:618-629.
[18] WU X,LIU M.In-situ soil moisture sensing:Measurement scheduling and estimation using compressive sensing[C]∥Proceedings of the 11th ACM International Conference on Information Processing in Sensor Networks.2012:1-12.
[19] HU S W,LIN G X,HSIEH S H,et al.Performance analysis of joint-sparse recovery from multiple measurement vectors with prior information via convex optimization[C]∥IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2016:4368-4372.
[20] ZHAO X,YANG Q,ZHANG Y.Synthesis of sparse linear array with multiple patterns based on joint sparse recovery[C]∥IEEE International Symposium on Antennas and Propagation & Usnc/ursi National Radio Science Meeting.IEEE,2017:425-426.
[21] WALEWSKI A C,STEFFENS C,PESAVENTO M.Off-Grid Parameter Estimation Based on Joint Sparse Regularization[C]∥International Itg Conference on Systems,Communications and Coding.2017.
[22] ZHANG Z,RAO B D.Sparse signal recovery in the presence of correlated multiple measurement vectors[C]∥International Conference on Acoustics Speech and Signal Processing.2010:3986-3989.
[23] PRASAD R,MURPHY C R,RAO B D.Joint approximately sparse channel estimation and data detection in OFDM systems using sparse Bayesian learning[J].IEEE Signal Processing Letters,2014,62(14):3591-3603.
[24] CHEN W.Simultaneous sparse Bayesian learning with partially shared support[J].IEEE Signal Processing Letters,2017,24(11):1641-1645.
[25] TIPPING M E.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2001,1(3):211-244.
[26] BISHOP C M.Pattern Recognition and Machine Learning (Information Science and Statistics).Springer-Verlag New York,2006.
[1] ZHANG Wang-ce, FAN Jing, WANG Bo-ru and NI Min. (α,k)-anonymized Model for Missing Data [J]. Computer Science, 2020, 47(6A): 395-399.
[2] SONG Xiao-xiang, GUO Yan, LI Ning, WANG Meng. Missing Data Prediction Based on Compressive Sensing in Time Series [J]. Computer Science, 2019, 46(6): 35-40.
[3] FAN Zhe-ning, YANG Qiu-hui, ZHAI Yu-peng, WAN Ying, WANG Shuai. Improved ROUSTIDA Algorithm for Missing Data Imputation with Key Attribute in Repetitive Data [J]. Computer Science, 2019, 46(2): 30-34.
[4] BIAN Xiao-li. Low Complexity Bayesian Sparse Signal Algorithm Based on Stretched Factor Graph [J]. Computer Science, 2018, 45(6A): 135-139.
[5] WANG Feng WEI Wei. Group Feature Selection Algorithm for Data Sets with Missing Data [J]. Computer Science, 2015, 42(7): 285-290.
[6] PENG Hong-Yi, ZHU Si-Ming, JIANG Chun-Fu (Department of Mathematics, Sun Yat-sen University, Guangahou 510275). [J]. Computer Science, 2005, 32(12): 203-205.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .