Moving intervals for nonlinear time series forecasting

Germán Sánchez, Albert Samà, Francisco J. Ruiz, Núria Agell

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Resum

In this paper a new forecasting methodology to be used on time series prediction is introduced. The considered nonlinear method is based on support vector machines (SVM) using an interval kernel. An extended intersection kernel is introduced to discriminate between disjoint intervals in reference to the existing distance among them. The model presented is applied to forecast exchange ratios using six world's major currencies. The results obtained show that SVMs based on interval kernel have a similar behavior than other SVM classical forecasting approaches, allowing its performance to be seen as very promising when using high frequency data.

Idioma originalAnglès
Títol de la publicacióArtificial Intelligence Research and Development
EditorIOS Press
Pàgines217-225
Nombre de pàgines9
ISBN (imprès)9781607506423
DOIs
Estat de la publicacióPublicada - 2010
Publicat externament

Sèrie de publicacions

NomFrontiers in Artificial Intelligence and Applications
Volum220
ISSN (imprès)0922-6389

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