Gaussian Regression based on Models with two Stochastic Processes
| Authors: | Leithead W. E., University of Strathclyde, United Kingdom
Seng Neo Kian, NUIM, Ireland
Leith D. J., NUIM, Ireland |
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| Topic: | 1.1 Modelling, Identification & Signal Processing |
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| Session: | Time Series Modelling |
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| Keywords: | Identification, Gaussian processes, independent priors, independent posteriors |
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Abstract
When data contains components with different characteristics and it is required to identify both, standard Gaussian regression, based on a model with a single stochastic process, is inadequate. In this paper, a novel adaptation of Gaussian regression, based on models with two stochastic processes, is presented. In both the prior and posterior joint probability distributions, the Gaussian processes for the two components are independent. The effectiveness of the revised Gaussian regression method is demonstrated by application to wind turbine time series data.
