Empirical Inference

Multiple-step ahead prediction for non linear dynamic systems: A Gaussian Process treatment with propagation of the uncertainty

2003

Conference Paper

ei


We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form y_t = f(y_{t-1},...,y_{t-L}), the prediction of y at time t + k is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction.

Author(s): Girard, A. and Rasmussen, CE. and Quiñonero-Candela, J. and Murray-Smith, R.
Book Title: Advances in Neural Information Processing Systems 15
Journal: Advances in Neural Information Processing Systems 15
Pages: 529-536
Year: 2003
Month: October
Day: 0
Editors: Becker, S. , S. Thrun, K. Obermayer
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-02550-7
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2105,
  title = {Multiple-step ahead prediction for non linear dynamic systems: A Gaussian Process treatment with propagation of the uncertainty},
  author = {Girard, A. and Rasmussen, CE. and Quiñonero-Candela, J. and Murray-Smith, R.},
  journal = {Advances in Neural Information Processing Systems 15},
  booktitle = {Advances in Neural Information Processing Systems 15},
  pages = {529-536},
  editors = {Becker, S. , S. Thrun, K. Obermayer},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = oct,
  year = {2003},
  doi = {},
  month_numeric = {10}
}