Kernel methods applied to time series forecasting

Authors:
Ginés Rubio;Héctor Pomares;Luis J. Herrera;Ignacio Rojas
Affiliations:
Dept. of Computer Architecture and Technology, University of Granada, Spain;Dept. of Computer Architecture and Technology, University of Granada, Spain;Dept. of Computer Architecture and Technology, University of Granada, Spain;Dept. of Computer Architecture and Technology, University of Granada, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 3
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Abstract

Kernel methods are a class of algorithms whose importance has grown from the 90s in the machine learning field. Their most notable example are Support Vector Machines (SVMs), which are the state of the art for classification problems. Nevertheless, they are applicable to functional approximation problems and there are however several of them available: SVM for regression, Gaussian Process Regression and Least Squares SVM (LS-SVM) for instance. This paper applies and studies these algorithms to a number of Time Series Prediction problems and compares them with some more conventional techniques.