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  Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting

Girard, A., Rasmussen, C., Quiñonero-Candela, J., & Murray-Smith, R. (2003). Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in Neural Information Processing Systems 15 (pp. 529-536). Cambridge, MA, USA: MIT Press.

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 Creators:
Girard, A, Author
Rasmussen, CE1, Author           
Quiñonero-Candela, J1, Author           
Murray-Smith, R, Author
Affiliations:
1External Organizations, ou_persistent22              

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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 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.

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 Dates: 2003-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2105
 Degree: -

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Title: Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2002-12-09 - 2002-12-14

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Title: Advances in Neural Information Processing Systems 15
Source Genre: Proceedings
 Creator(s):
Becker, S, Editor
Thrun, S, Editor
Obermayer, K, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 529 - 536 Identifier: ISBN: 0-262-02550-7