Self-organizing maps
Physica D
Nonlinear time series analysis
Nonlinear time series analysis
Neural Computation
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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-stepp rediction 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.