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A Partitioning Method for Fuzzy Probabilistic Predictors

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Neural Information Processing (ICONIP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3316))

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Abstract

We present a new partitioning method to determinate fuzzy regions in fuzzy probabilistic predictors. Fuzzy probabilistic predictors are modifications of discrete probabilistic classifiers, as Naive Bayes Classifier and Hidden Markov Model, in order to enable them to predict continuous values. Two fuzzy probabilistic predictors, Fuzzy Markov Predictor and the Fuzzy Hidden Markov Predictor, are applied to the task of monthly electric load single-step forecasting using this new partitioning and successfully compared with two Kalman Filter Models, and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing. The employed time series present a sudden significant changing behavior at their last years, as it occurs in an energy rationing.

The authors are partially financially supported by the Brazilian Research Agencies CAPES and CNPq, respectively.

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Author information

Authors and Affiliations

  1. Systems Engineering and Computer Science – COPPE, Federal University of Rio de Janeiro (UFRJ), P.O. Box 68511, ZIP 21945-970, Rio de Janeiro, Brazil

    Marcelo Andrade Teixeira & Gerson Zaverucha

  2. Electric Power Research Center (CEPEL), P.O. Box 68007, ZIP 21944-970, Rio de Janeiro, Brazil

    Marcelo Andrade Teixeira

Authors
  1. Marcelo Andrade Teixeira
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  2. Gerson Zaverucha
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Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Electronics and Communication Sciences Unit, Kolkata, India

    Nikhil Ranjan Pal

  2. School of Computer and Information Sciences, Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Private Bag 92006, Auckland, New Zealand

    Nik Kasabov

  3. Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt-lake Campus, 700098, Calcutta, India

    Rajani K. Mudi

  4. Indian Statistical Institute, 203 B. T. Road, 700 108, Calcutta,  

    Srimanta Pal

  5. Indian Statistical Institute, Computer Vision and Pattern Recognition Unit, 700108, Kolkata, India

    Swapan Kumar Parui

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© 2004 Springer-Verlag Berlin Heidelberg

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Teixeira, M.A., Zaverucha, G. (2004). A Partitioning Method for Fuzzy Probabilistic Predictors. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_143

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  • DOI: https://doi.org/10.1007/978-3-540-30499-9_143

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23931-4

  • Online ISBN: 978-3-540-30499-9

  • eBook Packages: Springer Book Archive

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