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 derived1.
We gratefully acknowledge the MWK, Lower Saxony, for financial support, and Michael Brandenburg, Dept. of Ecology, Münster, for supplying the runoff data.
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Strickert, M., Bojer, T., Hammer, B. (2001). Generalized Relevance LVQ for Time Series. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_94
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DOI: https://doi.org/10.1007/3-540-44668-0_94
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