Abstract
In This Paper, Variable Selection and Variable Scaling Are Used in Order to Select the Best Regressor for the Problem of Time Series Prediction. Direct Prediction Methodology is Used Instead of the Classic Recursive Methodology. Least Squares Support Vector Machines (LS-SVM) and K-NN Approximator Are Used in Order to Avoid Local Minimal in the Training Phase of the Model. the Global Methodology is Applied to the Estsp'07 Competition Dataset [1] and the Dataset B of the Nn3 Forecasting Competition [2]. ©2007 IEEE.
Recommended Citation
A. Lendasse and E. Liitiainen, "Variable Scaling for Time Series Prediction: Application to the ESTSP'07 and the NN3 Forecasting Competitions," IEEE International Conference on Neural Networks - Conference Proceedings, pp. 2812 - 2816, article no. 4371405, Institute of Electrical and Electronics Engineers, Dec 2007.
The definitive version is available at https://doi.org/10.1109/IJCNN.2007.4371405
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-142441380-5
International Standard Serial Number (ISSN)
1098-7576
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Dec 2007
