E-tsRBF: preliminary results on the simultaneous determination of time-lags and parameters of Radial Basis Function Neural Networks for time series forecasting

被引：0

作者：

Parras-Gutierrez, E. ^{[1
]}

Rivas, V. ^{[1
]}

del Jesus, M. J. ^{[1
]}

机构：

[1] Dept Comp Sci, Jaen 23071, Spain

来源：

2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS | 2009年

关键词：

Neural Network; evolutionary algorithms; time series;

D O I：

10.1109/ISDA.2009.234

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Radial basis function neural networks have been successfully applied to time series prediction in literature. Frequently, methods to build and train these networks must be given the past periods or lags to be used in order to create patterns and forecast any time series. This paper introduces E-tsRBF, a meta-evolutionary algorithm that evolves both the neural networks and the set of lags needed to forecast time series at the same time. Up to twenty-one time series are evaluated in this work, showing the behavior of the new method.

引用

页码：1445 / 1449

页数：5

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