Mathematical Programming: Series A and B
An Interior-Point Algorithm for Nonconvex Nonlinear Programming
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part II
Electricity Market Price Forecasting: Neural Networks versus Weighted-Distance k Nearest Neighbours
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Latent linkage semantic kernels for collective classification of link data
Journal of Intelligent Information Systems
Learning Contextual Dependency Network Models for Link-Based Classification
IEEE Transactions on Knowledge and Data Engineering
Two-phase Web site classification based on Hidden Markov Tree models
Web Intelligence and Agent Systems
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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This paper describes a time-series prediction method based on the k-Weighted Nearest Neighbours (k-WNN) algorithm and a simple technique to deal with nonconvex, nonlinear optimization problems by solving a sequence of Interior Point (IP) subproblems. The proposed prediction methodology is applied to obtain the 24-hour forecasts of two real time series: the demand and the energy prices in the competitive Spanish Electricity Market. The proposed optimization method is applied to the optimal scheduling of the electric energy production in the short-term.