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.
Keywords
Gaussian processSeries (stratigraphy)GaussianParametric statisticsComputer sciencePrior probabilityTime seriesState spaceMathematical optimizationProcess (computing)AlgorithmApplied mathematicsMathematicsArtificial intelligenceMachine learningStatisticsBayesian probability
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Publication Info
- Year
- 2002
- Type
- article
- Volume
- 15
- Pages
- 545-552
- Citations
- 370
- Access
- Closed
External Links
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Cite This
Agathe Girard,
Carl Edward Rasmussen,
Joaquin Quiñonero Candela
et al.
(2002).
Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting.
, 15
, 545-552.