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Abstract: 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 estimates ^ y t+k 1 ; : : : ; ^ y t+k L of the previous outputs. (Update)
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BibTeX entry: (Update)
A. Girard, C. E. Rasmussen, J. Quionero-Candela, and R. Murray-Smith. Multiple-step ahead prediction for non linear dynamic systems --- a gaussian process treatment with propagation of the uncertainty. In Suzanna Becker, Sebastian Thrun, and Klaus Obermayer, editors, Advances in Neural Information Processing Systems, volume 15, pages 529--536. MIT Press, 2003. http://citeseer.ist.psu.edu/girard03multiplestep.html More
@misc{ girard03multiplestep,
author = "A. Girard and C. Rasmussen and J. Quionero-Candela and R. Murray-Smith",
title = "Multiple-step ahead prediction for non linear dynamic systems --- a gaussian
process treatment with propagation of the uncertainty",
text = "A. Girard, C. E. Rasmussen, J. Quionero-Candela, and R. Murray-Smith. Multiple-step
ahead prediction for non linear dynamic systems --- a gaussian process treatment
with propagation of the uncertainty. In Suzanna Becker, Sebastian Thrun,
and Klaus Obermayer, editors, Advances in Neural Information Processing
Systems, volume 15, pages 529--536. MIT Press, 2003.",
year = "2003",
url = "citeseer.ist.psu.edu/girard03multiplestep.html" }
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