Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/21407

TitleEvolutionary support vector machines for time series forecasting
Author(s)Cortez, Paulo
Peralta Donate, Juan
KeywordsEvolutionary computation
Support vector machines
Time series
Forecasting
Issue dateSep-2012
PublisherSpringer
JournalLecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract(s)Abstract. Time Series Forecasting (TSF) uses past patterns of an event in order to predict its future values and is a key tool to support decision making. In the last decades, Computational Intelligence (CI) techniques, such as Artificial Neural Networks (ANN) and more recently Support Vector Machines (SVM), have been proposed for TSF. The accuracy of the best CI model is affected by both the selection of input time lags and the model’s hyperparameters. In this work, we propose a novel Evolutionary SVM (ESVM) approach for TSF based on the Estimation Distribution Algorithm to search for the best number of inputs and SVM hyperparameters. Several experiments were held, using a set of six time series from distinct real-world domains. Overall, the proposed ESVM is competitive when compared with an Evolutionary ANN (EANN) and the popular ARIMA methodology, while consuming less computational effort when compared with EANN.
TypeConference paper
URIhttp://hdl.handle.net/1822/21407
ISBN978-3-642-33265-4
DOI10.1007/978-3-642-33266-1_65
ISSN0302-9743
Publisher versionhttp://link.springer.com/
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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