Weighted Cross-Validation Evolving Artificial Neural Networks to Forecast Time Series View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Juan Peralta Donate , Paulo Cortez , German Gutierrez Sanchez , Araceli Sanchis de Miguel

ABSTRACT

Accurate time series forecasting is a key tool to support decision making and for planning our day to-day activities. In recent years, several works in the literature have adopted evolving artificial neural networks (EANN) for forecasting applications. EANNs are particularly appealing due to their ability to model an unspecified non-linear relationship between time series variables. In this work, a novel approach for EANN forecasting systems is proposed, where a weighted cross-validation is used to build an ensemble of neural networks. Several experiments were held, using a set of six real-world time series (from different domains) and comparing both the weighted and standard cross-validation variants. Overall, the weighted cross-validation provided the best forecasting results. More... »

PAGES

147-154

References to SciGraph publications

  • 2006. A k-Sample Slippage Test for an Extreme Population in SELECTED PAPERS OF FREDERICK MOSTELLER
  • Book

    TITLE

    Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011

    ISBN

    978-3-642-19643-0
    978-3-642-19644-7

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-19644-7_16

    DOI

    http://dx.doi.org/10.1007/978-3-642-19644-7_16

    DIMENSIONS

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