Multilayer feedforward networks are universal approximators
Neural Networks
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural networks for pattern recognition
Neural networks for pattern recognition
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
An introduction to variable and feature selection
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Analysis of fast input selection: application in time series prediction
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
The curse of dimensionality in data mining and time series prediction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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
Multivariate Student-t self-organizing maps
Neural Networks
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Gene selection in time-series gene expression data
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
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, making accurate predictions is often the primary goal. At the same time, interpretability of the models would be desirable. For the latter goal, we have devised a sequential input selection algorithm (SISAL) to choose a parsimonious, or sparse, set of input variables. Our proposed algorithm is a sequential backward selection type algorithm based on a cross-validation resampling procedure. Our strategy is to use a filter approach in the prediction: first we select a sparse set of inputs using linear models and then the selected inputs are used in the nonlinear prediction conducted with multilayer-perceptron networks. Furthermore, we perform a sensitivity analysis by quantifying the importance of the individual input variables in the nonlinear models using a method based on partial derivatives. Experiments are done with the Santa Fe laser data set that exhibits very nonlinear behavior and a data set in a problem of electricity load prediction. The results in the prediction problems of varying difficulty highlight the range of applicability of our proposed algorithm. In summary, our SISAL yields accurate and parsimonious prediction models giving insight to the original problem.