Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/14844

TitleWeighted cross-validation evolving artificial neural networks to forecast time series
Author(s)Peralta Donate, Juan
Cortez, Paulo
Gutierrez Sanchez, German
Sanchis de Miguel, Araceli
KeywordsEvolutionary computation
Genetic algorithms
Artificial neural networks
Time series
Forecasting
Ensembles
Issue dateApr-2011
PublisherSpringer
JournalAdvances in Intelligent and Soft Computing
Abstract(s)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.
TypeConference paper
URIhttp://hdl.handle.net/1822/14844
ISBN978-3-642-19643-0
DOI10.1007/978-3-642-19644-7_16
ISSN1867-5662
Publisher versionhttp://www.springerlink.com/index/JN5578W43455W60Q.pdf
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals
DSI - Engenharia da Programação e dos Sistemas Informáticos

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