On incorporating seasonal information on recursive time series predictors

Authors:
Luis Javier Herrera;Hector Pomares;Ignacio Rojas;Alberto Guilén;G. Rubio
Affiliations:
Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain;Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain;Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain;Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain;Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 5
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Abstract

In time series prediction problems in which the current series presents a certain seasonality, the long term and short term prediction capabilities of a learned model can be improved by considering that seasonality as additional information within it. Kernel methods and specifically LS-SVM are receiving increasing attention in the last years thanks to many interesting properties; among them, these type of models can include any additional information by simply adding new variables to the problem, without increasing the computational cost. This work evaluates how including the seasonal information of a series in a designed recursive model might not only upgrade the performance of the predictor, but also allows to diminish the number of input variables needed to perform the modelling, thus being able to increase both the generalization and interpretability capabilities of the model.