Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting
Abstract
We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. -step ahead forecasting of a discrete-time non-linear dynamic system can be per- formed by doing repeated one-step ahead predictions. For a state-space at time model of the form is based on the point estimates of the previous outputs. In this pa- per, 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.
Cite
Text
Girard et al. "Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting." Neural Information Processing Systems, 2002.Markdown
[Girard et al. "Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/girard2002neurips-gaussian/)BibTeX
@inproceedings{girard2002neurips-gaussian,
title = {{Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting}},
author = {Girard, Agathe and Rasmussen, Carl Edward and Candela, Joaquin Quiñonero and Murray-Smith, Roderick},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {545-552},
url = {https://mlanthology.org/neurips/2002/girard2002neurips-gaussian/}
}