Amaximization of the expected entropy of the predictive distribution interpretation of Akaike's minimum AIC procedure is exploited for the modeling and prediction of time series with trend and seasonal mean value functions and stationary covariances. The AIC criterion best one-step-ahead and best twelvestep-ahead prediction models are different. They exhibit the relative optimality properties f...

The aim of this paper is to prove the validity of an alternative prediction technique to another classical one, which is Box-Jenkins methodology, in order to produce multivariate prediction. In particular, one-step ahead forecasts will be obtained for two time series: thermic and hydraulic power production. These forecasts are based on the past values of those series.

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Combining neural networks and wavelet theory as an approximation or prediction models appears to be an effective solution in many applicative areas. However, when building such systems, one has to face parsimony problem, i.e., to look for a compromise between the complexity of the learning phase and accuracy performances. Following that, the aim of this paper is to propose a new structure of co...

In this paper we describe the winning entry of the time series prediction competition which was part of the International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling , held at K.U. Leuven, Belgium on July 8–10, 1998. We also describe the source of the data set, a nonlinear transform of a 5-scroll generalized Chua’s circuit. Participants were given 2000 data points and were ...

In this paper, the estimation of prediction intervals for multi-step-ahead predictions from dynamic neural network models is described. Usually, asymptotic methods based on linearizations are applied with the potential problem of large coverage errors and too optimistic prediction intervals. The potential sources of these problems are the negligence of the network parameter uncertainties and th...

hydropower reservoirs Georgia Papacharalampous*, Hristos Tyralis, and Demetris Koutsoyiannis Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, Greece * Corresponding author, [email protected] Abstract: Multi-step ahead streamflow forecasting is of practical i...

Dyna planning is an efficient way of learning from real and imaginary experience. Existing tabular and linear Dyna algorithms are single-step, because an “imaginary” feature is predicted only one step into the future. In this paper, we introduce a multi-step Dyna planning that predicts more steps into the future. Multi-step Dyna is able to figure out a sequence of multi-step results when a real...