An Efficient Missing Data Prediction Method Based on Kronecker Compressive Sensing in Multivariable Time Series
The existence of missing data severely affects the establishment of correct data mining model from the raw data. Unfortunately, most of the existing missing data prediction approaches are inefficient to predict missing data from multivariable time series due to the low accuracy and poor stability pr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8478266/ |