Computers and Operations Research
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
On global, local, mixed and neighborhood kernels for support vector machines
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Comparison of Two Techniques for Next-Day Electricity Price Forecasting
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Creation of Specific-to-Problem Kernel Functions for Function Approximation
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
On incorporating seasonal information on recursive time series predictors
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Mutual information and k-nearest neighbors approximator for time series prediction
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
IEEE Transactions on Neural Networks
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Least squares support vector machines (LSSVM) with Gaussian kernel represent the most used of the kernel methods existing in the literature for regression and time series prediction. These models have a good behaviour for these types of problems due to their generalization capabilities and their smooth interpolation, but they are very dependent on the feature selection performed and their computational cost notably increases with the number of training samples. Time series prediction can be tackled as a regression problem by constructing a set of input/output data from the series; this traditional approach and the use of typical recursive or direct strategies present serious drawbacks in long-term prediction. This paper presents an alternative based on the settings of specific-to-problem kernels to be applied to time series prediction focusing on large scale prediction. A simple methodology for kernel creation based on the periodicities in time series data is proposed. An alternative to LSSVM models with lower computational cost, the Kernel Weighted K-Nearest Neighbours (KWKNN) is described for function approximation. A parallel version of KWKNN is also presented to deal with large data sets.