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ICANN
2001
Springer

Generalized Relevance LVQ for Time Series

9 years 3 months ago
Generalized Relevance LVQ for Time Series
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 wellknown 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
Marc Strickert, Thorsten Bojer, Barbara Hammer
Added 29 Jul 2010
Updated 29 Jul 2010
Type Conference
Year 2001
Where ICANN
Authors Marc Strickert, Thorsten Bojer, Barbara Hammer
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