Input Selection for Long-Term Prediction of Time Series
Abstract
Prediction of Time Series is an Important Problem in Many Areas of Science and Engineering. Extending the Horizon of Predictions Further to the Future is the Challenging and Difficult Task of Long-Term Prediction. in This Paper, We Investigate the Problem of Selecting Non-Contiguous Input Variables for an Autoregressive Prediction Model in Order to Improve the Prediction Ability. We Present an Algorithm in the Spirit of Backward Selection Which Removes Variables Sequentially from the Prediction Models based on the Significance of the Individual Regressors. We Successfully Test the Algorithm with a Non-Linear System by Selecting Inputs with a Linear Model and Finally Train a Non-Linear Predictor with the Selected Variables on Santa Fe Laser Data Set. © Springer-Verlag Berlin Heidelberg 2005.
Recommended Citation
J. Tikka et al., "Input Selection for Long-Term Prediction of Time Series," Lecture Notes in Computer Science, vol. 3512, pp. 1002 - 1009, Springer, Jan 2005.
The definitive version is available at https://doi.org/10.1007/11494669_123
Department(s)
Engineering Management and Systems Engineering
International Standard Serial Number (ISSN)
0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2005
