Anomaly detection and removal using non-stationary Gaussian processes
This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and model the signal by two separate non-parametric Ga...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Cornell University
2015
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