Neural Processing Letters

, Volume 3, Issue 2, pp 101–106 | Cite as

Use of modular architectures for time series prediction

  • Samy Bengio
  • Françoise Fessant
  • Daniel Collobert

Abstract

Recently, a lot of papers have been published in the field of time series prediction using connectionist models. Nevertheless we think that one of the major problem with is rarely treated in the literature is related to the choice of input parameters (embedding dimension and delay). In this paper, we propose two modular approaches to this problem and apply them to a sunspot-related time series. Experimental results are then compared to a single multi-layer perceptron in order to estimate performances of these models.

Key words

embedding dimension mixtures of experts modular architectures time-series prediction 

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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Samy Bengio
    • 1
  • Françoise Fessant
    • 2
  • Daniel Collobert
    • 3
  1. 1.Dept. IROUniversité de MontréalMontréalCanada
  2. 2.LEIBNIZ-IMAGGrenoble CédexFrance
  3. 3.LAB/RIO/TNTFrance Télécom CNETLannion CédexFrance

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