Abstract
We Apply Fuzzy Techniques for System Identification and Supervised Learning in Order to Develop Fuzzy Inference based Auto regressors for Time Series Prediction. an Automatic Methodology Framework that Combines Fuzzy Techniques and Statistical Techniques for Nonparametric Residual Variance Estimation is Proposed. Identification is Performed through the Learn from Examples Method Introduced by Wang and Mendel, While the Marquard-Levenberg Supervised Learning Algorithm is Then Applied for Tuning. Delta Test Residual Noise Estimation is Used in Order to Select the Best Subset of Inputs as Well as the Number of Linguistic Labels for the Inputs. Experimental Results for Three Time Series Prediction Benchmarks Are Compared Against Ls-SVM based Auto regressors and Show the Advantages of the Proposed Methodology in Terms of Approximation Accuracy, Generalization Capability and Linguistic Interpretability. © 2008 IEEE.
Recommended Citation
F. M. Pouzols et al., "Fuzzy Inference based Autoregressors for Time Series Prediction using Nonparametric Residual Variance Estimation," IEEE International Conference on Fuzzy Systems, pp. 613 - 618, article no. 4630432, Institute of Electrical and Electronics Engineers, Nov 2008.
The definitive version is available at https://doi.org/10.1109/FUZZY.2008.4630432
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-142441819-0
International Standard Serial Number (ISSN)
1098-7584
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
07 Nov 2008
