Gaussian Process priors with uncertain inputs? Application to multiple-step ahead time series forecasting

Girard, A., Rasmussen, C.E., Quinonero-Candela, J. and Murray-Smith, R. (2003) Gaussian Process priors with uncertain inputs? Application to multiple-step ahead time series forecasting. In: Neural Information Processing Systems, Vancouver, Canada, 9-14 December 2002, ISBN 0262025507

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(Yt-1 ,..., Yt-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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Murray-Smith, Professor Roderick
• Authors: Girard, A., Rasmussen, C.E., Quinonero-Candela, J., and Murray-Smith, R.
• Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: MIT Press
• ISBN: 0262025507
• Copyright Holders: Copyright © 2002 MIT Press
• First Published: First published in Advances in neural information processing systems: 15 proceedings of the 2002 Neural Information Processing Systems Conference
• Publisher Policy: Reproduced with the permission of the publisher