Generalized Relevance LVQ for Time Series
Strickert M, Bojer T, Hammer B (2001)
In: Artificial Neural Networks — ICANN 2001. Dorffner G, Bischof H, Hornik K (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 677-683.
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Autor*in
Strickert, Marc;
Bojer, Thorsten;
Hammer, BarbaraUniBi 
Herausgeber*in
Dorffner, Georg;
Bischof, Horst;
Hornik, Kurt
Einrichtung
Abstract / Bemerkung
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.
Erscheinungsjahr
2001
Buchtitel
Artificial Neural Networks — ICANN 2001
Serientitel
Lecture Notes in Computer Science
Seite(n)
677-683
ISBN
978-3-540-42486-4
eISBN
978-3-540-44668-2
ISSN
0302-9743
Page URI
https://pub.uni-bielefeld.de/record/2982129
Zitieren
Strickert M, Bojer T, Hammer B. Generalized Relevance LVQ for Time Series. In: Dorffner G, Bischof H, Hornik K, eds. Artificial Neural Networks — ICANN 2001. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2001: 677-683.
Strickert, M., Bojer, T., & Hammer, B. (2001). Generalized Relevance LVQ for Time Series. In G. Dorffner, H. Bischof, & K. Hornik (Eds.), Lecture Notes in Computer Science. Artificial Neural Networks — ICANN 2001 (pp. 677-683). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-44668-0_94
Strickert, Marc, Bojer, Thorsten, and Hammer, Barbara. 2001. “Generalized Relevance LVQ for Time Series”. In Artificial Neural Networks — ICANN 2001, ed. Georg Dorffner, Horst Bischof, and Kurt Hornik, 677-683. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg.
Strickert, M., Bojer, T., and Hammer, B. (2001). “Generalized Relevance LVQ for Time Series” in Artificial Neural Networks — ICANN 2001, Dorffner, G., Bischof, H., and Hornik, K. eds. Lecture Notes in Computer Science (Berlin, Heidelberg: Springer Berlin Heidelberg), 677-683.
Strickert, M., Bojer, T., & Hammer, B., 2001. Generalized Relevance LVQ for Time Series. In G. Dorffner, H. Bischof, & K. Hornik, eds. Artificial Neural Networks — ICANN 2001. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 677-683.
M. Strickert, T. Bojer, and B. Hammer, “Generalized Relevance LVQ for Time Series”, Artificial Neural Networks — ICANN 2001, G. Dorffner, H. Bischof, and K. Hornik, eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2001, pp.677-683.
Strickert, M., Bojer, T., Hammer, B.: Generalized Relevance LVQ for Time Series. In: Dorffner, G., Bischof, H., and Hornik, K. (eds.) Artificial Neural Networks — ICANN 2001. Lecture Notes in Computer Science. p. 677-683. Springer Berlin Heidelberg, Berlin, Heidelberg (2001).
Strickert, Marc, Bojer, Thorsten, and Hammer, Barbara. “Generalized Relevance LVQ for Time Series”. Artificial Neural Networks — ICANN 2001. Ed. Georg Dorffner, Horst Bischof, and Kurt Hornik. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. Lecture Notes in Computer Science. 677-683.
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