Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/31000

TitleGlobal and decomposition evolutionary support vector machine approaches for time series forecasting
Author(s)Cortez, Paulo
Donate, Juan Peralta
KeywordsEstimation distribution algorithm
Support vector machines
Time series
Decomposition forecasting
Model selection
Issue dateOct-2014
PublisherSpringer Verlag
JournalNeural Computing and Applications
Abstract(s)Multi-step ahead Time Series Forecasting (TSF) is a key tool for support- ing tactical decisions (e.g., planning resources). Recently, the support vector machine emerged as a natural solution for TSF due to its nonlinear learning capabilities. This paper presents two novel Evolutionary Support Vector Machine (ESVM) methods for multi-step TSF. Both methods are based on an Estimation Distribution Algorithm (EDA) search engine that automatically performs a simultaneous variable (number of inputs) and model (hyperparameters) selection. The Global ESVM (GESVM) uses all past patterns to fit the support vector machine, while the Decomposition ESVM (DESVM) separates the series into trended and stationary effects, using a distinct ESVM to forecast each effect and then summing both predictions into a sin- gle response. Several experiments were held, using six time series. The proposed approaches were analyzed under two criteria and compared against a recent Evolu- tionary Artificial Neural Network (EANN) and two classical forecasting methods, Holt-Winters and ARIMA. Overall, the DESVM and GESVM obtained competitive and high quality results. Furthermore, both ESVM approaches consume much less computational effort when compared with EANN.
TypeArticle
URIhttp://hdl.handle.net/1822/31000
DOI10.1007/s00521-014-1593-1
ISSN0941-0643
Publisher versionThe original publication is available at: http://link.springer.com/article/10.1007/s00521-014-1593-1
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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