Ls-Svm Functional Network for Time Series Prediction
Abstract
Usually Time Series Prediction is Done with Regularly Sampled Data. in Practice, However, the Data Available May Be Irregularly Sampled. in This Case the Conventional Prediction Methods Cannot Be Used. One Solution is to Use Functional Data Analysis (Fda). in Fda an Interpolating Function is Fitted to the Data and the Fitting Coefficients Are Being Analyzed Instead of the Original Data Points. in This Paper, We Propose a Functional Approach to Time Series Prediction. Radial Basis Function Network (Rbfn) is Used for the Interpolation. the Interpolation Parameters Are Optimized with a K-Nearest Neighbors (K-Nn) Model. Least Squares Support Vector Machine (Ls-Svm) is Used for the Prediction.
Recommended Citation
T. Kärnä et al., "Ls-Svm Functional Network for Time Series Prediction," ESANN 2006 Proceedings - European Symposium on Artificial Neural Networks, pp. 473 - 478, European Symposium on Artificial Neural Networks, Jan 2006.
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-293030706-0
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 Jan 2006
