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Intelligent Forecast with Dimension Reduction

  • Conference paper
Book cover Applied Soft Computing Technologies: The Challenge of Complexity

Part of the book series: Advances in Soft Computing ((AINSC,volume 34))

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Abstract

Time-series prediction can be interpreted in a way that is suitable for artificial intelligence learning. Two effective learning methods, Artificial Neural Networks and Support Vector Machines, are used to provide accurate non-linear models of the problem. In spite of the effectiveness of these methods we have to solve two problems. Firstly, time-series can have noise and a high dimensional embedding space. Secondly, the learning depends on several hyper-parameters that need to be set properly. To handle these problems we apply noise and dimension reduction tech- niques and model selection to get suitable hyper-parameters. Then, we introduce a meta-heuristic to refine the predictions of the selected models. Our experiments show improvements in the quality of predictions of a real-life problem compared to two ‘benchmark’ algorithms.

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Author information

Authors and Affiliations

  1. Department of Computer Algorithms and Arti.cial Intelligence, University of Szeged and Research Group on Artificial Intelligence, Hungarian Academy of Science, Szeged, Hungary

    István Juhos & György Szarvas

Authors
  1. István Juhos
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  2. György Szarvas
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Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, Chung-Ang University, Heukseok-dong 221, 156-756, Seoul, Korea

    Ajith Abraham

  2. Department of Applied Mathematics Biometrics and Process Control, University Gent, Coupure Links 653, 9000 Gent, Belgium

    Bernard de Baets

  3. Dept. Automation Technologies, Fraunhofer IPK Berlin, Pascalstr. 8-9, 10587, Berlin, Germany

    Mario Köppen

  4. Dept. Automation Technologies, Fraunhofer IPK Berlin, Pascalstr. 8-9, 10587, Berlin, Germany

    Bertram Nickolay

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© 2006 Springer

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Juhos, I., Szarvas, G. (2006). Intelligent Forecast with Dimension Reduction. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_22

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  • DOI: https://doi.org/10.1007/3-540-31662-0_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31649-7

  • Online ISBN: 978-3-540-31662-6

  • eBook Packages: EngineeringEngineering (R0)

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