Konferenzpaper

Jahr der Veröffentlichung: 2001

Seiten: 677-683

Zeitschrift: Lecture notes in computer science

Bandnummer: 2130

ISSN: 0302-9743

ISBN: 3-540-42486-5

eISSN: 1611-3349

DOI Link: https://doi.org/10.1007/3-540-44668-0_94

Konferenz: International Conference on Artificial Neural Networks (ICANN 2001)

Verlag: Springer

Serientitel: Lecture Notes in Computer Science

Abstract: 

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.

Harvard-Zitierstil: Strickert, M., Bojer, T. and Hammer, B. (2001) Generalized relevance LVQ for time series, Lecture notes in computer science, 2130, pp. 677-683. https://doi.org/10.1007/3-540-44668-0_94

APA-Zitierstil: Strickert, M., Bojer, T., & Hammer, B. (2001). Generalized relevance LVQ for time series. Lecture notes in computer science. 2130, 677-683. https://doi.org/10.1007/3-540-44668-0_94