Journal article
Anomaly detection and removal using non-stationary Gaussian processes
- Abstract:
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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 Gaussian process models for both the physical phenomenon and the fault. In order to facilitate fault removal we introduce the Markov Region Link kernel for handling non-stationary Gaussian processes....
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- Publication status:
- Not published
- Peer review status:
- Not peer reviewed
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- Files:
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(Author's original, pdf, 214.7KB)
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- Publication website:
- https://arxiv.org/abs/1507.00566
Authors
Bibliographic Details
- Publisher:
- Cornell University Publisher's website
- Journal:
- arXiv Journal website
- Publication date:
- 2015-07-02
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
319017
- Local pid:
- pubs:319017
- Deposit date:
- 2023-01-20
Terms of use
- Copyright holder:
- Reece et al.
- Copyright date:
- 2015
- Rights statement:
- © 2015 The Authors. This is an arXiv preprint and is available at: https://arxiv.org/abs/1507.00566
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