Abstract
The aim of this paper is to present applicable, working pattern recognition system, which can find and classify all useful dependencies between data entries in time series. The idea of predictor and its level of certainty are introduced in the work. Genetic algorithm has been deployed to prepare and govern a set of independent predictors. Practical part of solution consists of data fitting and prediction. Architecture of the system offers possibility to interleave learning phase with use. Analyzed data may be non continuous, and incomplete. In uncertain cases the system presents either more than one answer to processed data or no response at all. Early testing results, including prediction and fitting of simple time series with missing data amount ranging from 10 to 50 percent, are presented at the end of this paper.
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© 2005 Springer-Verlag Berlin Heidelberg
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Borkowski, M. (2005). Time Series Patterns Recognition with Genetic Algorithms. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_11
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DOI: https://doi.org/10.1007/3-540-32390-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25054-8
Online ISBN: 978-3-540-32390-7
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