Generalized relevance LVQ for time series

Autor(en): Strickert, M
Bojer, T
Hammer, B
Herausgeber: Dorffner, G
Bischof, H
Hornik, K
Stichwörter: Computer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods
Erscheinungsdatum: 2001
Herausgeber: SPRINGER-VERLAG BERLIN
Journal: ARTIFICIAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS
Lecture Notes in Computer Science
Volumen: 2130
Startseite: 677
Seitenende: 683
Zusammenfassung: 
An application of the recently proposed generalized relevance learning vector quantization (GRLVQ) to the analysis and modeling of time series data is presented. We use GRLVQ for two tasks: first, for obtaining a phase space embedding of a scalar time series, and second, for short term and long term data prediction. The proposed embedding method is tested with a signal from the well-known Lorenz system. Afterwards, it is applied to daily lysimeter observations of water runoff. A one-step prediction of the runoff dynamic is obtained from the classification of high dimensional subseries data vectors, from which a promising technique for long term forecasts is derived.(1).
Beschreibung: 
International Conference on Artificial Neural Networks (ICANN 2001), VIENNA UNIV TECHNOL, VIENNA, AUSTRIA, AUG 21-25, 2001
ISBN: 9783540424864
ISSN: 03029743

Show full item record

Google ScholarTM

Check

Altmetric