| Citation: | Yan GUO, Xiaoxiang SONG, Ning LI, Peng QIAN. Missing Data Prediction Based on Kronecker Compressing Sensing in Multivariable Time Series[J]. Journal of Electronics & Information Technology, 2019, 41(4): 858-864. doi: 10.11999/JEIT180541
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In view of the problem that the existing methods are not applicable or are only feasible to the case where only a low ratio of data are missing in multivariable time series, a missing data prediction algorithm is proposed based on Kronecker Compressed Sensing (KCS) theory. Firstly, the sparse representation basis is designed to largely utilize both the temporal smoothness characteristic of time series and potential correlation between multiple time series. In this way, the missing data prediction problem is modeled into the problem of sparse vector recovery. In the solution part of the model, according to the location of missing data, the measurement matrix is designed suitable for the current application scenario and low correlation with the sparse representation basis. Then, the validity of the model is verified from two aspects: Whether the sparse representation vector is sufficiently sparse and the sensing matrix satisfies the restricted isometry property. Simulation results show that the proposed algorithm has good performance in the case where a high ratio of data are missing.
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