uncertain input application
Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting
We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. 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.
Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting
Girard, Agathe, Rasmussen, Carl Edward, Candela, Joaquin Quiñonero, Murray-Smith, Roderick
W e consider the problem of multi-step ahead prediction in time series analysis using the nonparametric Gaussian process model.
Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting
Girard, Agathe, Rasmussen, Carl Edward, Candela, Joaquin Quiñonero, Murray-Smith, Roderick
W e consider the problem of multi-step ahead prediction in time series analysis using the nonparametric Gaussian process model.
Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting
Girard, Agathe, Rasmussen, Carl Edward, Candela, Joaquin Quiñonero, Murray-Smith, Roderick
We consider the problem of multi-step ahead prediction in time series analysis using the nonparametric Gaussian process model.