Corpus ID: 88521281

Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes

@article{Ambrogioni2016AnalysisON,
  title={Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes},
  author={L. Ambrogioni and E. Maris},
  journal={arXiv: Machine Learning},
  year={2016}
}
  • L. Ambrogioni, E. Maris
  • Published 2016
  • Mathematics, Computer Science
  • arXiv: Machine Learning
  • The analysis of nonstationary time series is of great importance in many scientific fields such as physics and neuroscience. In recent years, Gaussian process regression has attracted substantial attention as a robust and powerful method for analyzing time series. In this paper, we introduce a new framework for analyzing nonstationary time series using locally stationary Gaussian process analysis with parameters that are coupled through a hidden Markov model. The main advantage of this… CONTINUE READING
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