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Time Series Prediction with Periodic Kernels

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Computer Recognition Systems 4

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 95))

Abstract

This short article presents the new algorithm of time series prediction: PerKE. It implements the kernel regression for the time series directly without any data transformation. This method is based on the new type of kernel function – periodic kernel function – which two examples are also introduced in this paper. This new algorithm belongs to the group of semiparametric methods as it needs the initial step that separate the trend from the original time series.

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Authors and Affiliations

  1. Central Mining Institute, Plac Gwarkow 1, 40-166, Katowice, Poland

    Marcin Michalak

  2. Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Marcin Michalak

Authors
  1. Marcin Michalak
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Editors and Affiliations

  1. Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Robert Burduk , Marek Kurzyński  & Andrzej Żołnierek ,  & 

  2. Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

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Michalak, M. (2011). Time Series Prediction with Periodic Kernels. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20320-6_15

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  • DOI: https://doi.org/10.1007/978-3-642-20320-6_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20319-0

  • Online ISBN: 978-3-642-20320-6

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