Multilayer feedforward networks are universal approximators
Neural Networks
Neural networks for pattern recognition
Neural networks for pattern recognition
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Input selection for long-term prediction of time series
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Direct and recursive prediction of time series using mutual information selection
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Combined input variable selection and model complexity control for nonlinear regression
Pattern Recognition Letters
Time Series Prediction Based on Generalization Bounds for Support Vector Machine
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Compact and understandable descriptions of mixtures of Bernoulli distributions
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Input selection for radial basis function networks by constrained optimization
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Fast variable selection for memetracker phrases time series prediction
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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In time series prediction, accuracy of predictions is often the primary goal. At the same time, however, it would be very desirable if we could give interpretation to the system under study. For this goal, we have devised a fast input selection algorithm to choose a parsimonious, or sparse set of input variables. The method is an algorithm in the spirit of backward selection used in conjunction with the resampling procedure. In this paper, our strategy is to select a sparse set of inputs using linear models and after that the selected inputs are also used in the non-linear prediction based on multi-layer perceptron networks. We compare the prediction accuracy of our parsimonious non-linear models with the linear models and the regularized non-linear perceptron networks. Furthermore, we quantify the importance of the individual input variables in the non-linear models using the partial derivatives. The experiments in a problem of electricity load prediction demonstrate that the fast input selection method yields accurate and parsimonious prediction models giving insight to the original problem.