Pruned Lazy Learning Models for Time Series Prediction
Abstract
This Paper Presents Two Improvements of Lazy Learning. Both Methods Include Input Selection and Are Applied to Long-Term Prediction of Time Series. First Method is based on an Iterative Pruning of the Inputs and the Second One is Performing a Brute Force Search in the Possible Set of Inputs using a K-Nn Approximator. Two Benchmarks Are Used to Illustrate the Efficiency of These Two Methods: The Santa Fe a Time Series and the Cats Benchmark Time Series.
Recommended Citation
A. Sorjamaa et al., "Pruned Lazy Learning Models for Time Series Prediction," ESANN 2005 Proceedings - 13th European Symposium on Artificial Neural Networks, pp. 509 - 514, European Symposium on Artificial Neural Networks, Dec 2007.
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-293030705-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 European Symposium on Artificial Neural Networks, All rights reserved.
Publication Date
01 Dec 2007
