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Time Series Prediction with the Self-Organizing Map: A Review

  • Chapter
Perspectives of Neural-Symbolic Integration

Part of the book series: Studies in Computational Intelligence ((SCI,volume 77))

We provide a comprehensive and updated survey on applications of Kohonen’s self-organizing map (SOM) to time series prediction (TSP). The main goal of the paper is to show that, despite being originally designed as an unsupervised learning algorithm, the SOM is flexible enough to give rise to a number of efficient supervised neural architectures devoted to TSP tasks. For each SOM-based architecture to be presented, we report its algorithm implementation in detail. Similarities and differences of such SOM-based TSP models with respect to standard linear and nonlinear TSP techniques are also highlighted. We conclude the paper with indications of possible directions for further research on this field.

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Authors and Affiliations

  1. Department of Teleinformatics Engineering, Federal University of Ceará, Av. Mister Hull, S/N, 6005, CEP 60455-760, Fortaleza, Ceará, Brazil

    Guilherme A. Barreto

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  1. Guilherme A. Barreto
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Editors and Affiliations

  1. University Clausthal, Julius Albert Strabe 4, 38678, Clausthal-Zellerfeld, Germany

    Barbara Hammer

  2. University of Karlsruhe, 76128, Karlsruhe, Germany

    Pascal Hitzler

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Barreto, G.A. (2007). Time Series Prediction with the Self-Organizing Map: A Review. In: Hammer, B., Hitzler, P. (eds) Perspectives of Neural-Symbolic Integration. Studies in Computational Intelligence, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73954-8_6

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  • DOI: https://doi.org/10.1007/978-3-540-73954-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73953-1

  • Online ISBN: 978-3-540-73954-8

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