Bayesian learning for neural networks
Bayesian learning for neural networks
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Methodology for long-term prediction of time series
Neurocomputing
A comparison between neural-network forecasting techniques-case study: river flow forecasting
IEEE Transactions on Neural Networks
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Gaussian process (GP), as one of the cornerstones of Bayesian non-parametric methods, has received extensive attention in machine learning. GP has intrinsic advantages in data modeling, given its construction in the framework of Bayesian hieratical modeling and no requirement for a priori information of function forms in Bayesian reference. In light of its success in various applications, utilizing GP for time-series forecasting has gained increasing interest in recent years. This paper is concerned with using GP for multiple-step-ahead time-series forecasting, an important type of time-series analysis. Utilizing a large number of real-world time series, this paper evaluates two different GP modeling strategies (direct and recursive) for performing multiple-step-ahead forecasting.