Time Series Forecasting by means of Evolutionary Algorithms

Many physical and artificial phenomena can be described by time series. The prediction of such phenomenon could be as complex as interesting. There are many time series forecasting methods, but most of them only look for general rules to predict the whole series. The main problem is that time series usually have local behaviours that don't allow forecasting the time series by general rules. In this paper, a new method for finding local prediction rules is presented. Those local prediction rules can attain a better general prediction accuracy. The method presented in this paper is based on the evolution of a rule system encoded following a Michigan approach. For testing this method, several time series domains have been used: a widely known artificial one, the Mackey-Glass time series, and two real world ones, the Venice Lagon and the sunspot time series.

[1]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

[2]  Inés María Galván,et al.  A Selective Learning Method to Improve the Generalization of Multilayer Feedforward Neural Networks , 2001, Int. J. Neural Syst..

[3]  Y Lu,et al.  A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks , 1997, Neural Computation.

[4]  A. Tomasin,et al.  A computer simulation of the adriatic sea for the study of its dynamics and for the forecasting of floods in the town of Venice , 1973 .

[5]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[6]  Norman H. Packard,et al.  A Genetic Learning Algorithm for the Analysis of Complex Data , 1990, Complex Syst..

[7]  Kenneth A. De Jong,et al.  Using genetic algorithms for concept learning , 1993, Machine Learning.

[8]  K. De Jong,et al.  Using Genetic Algorithms for Concept Learning , 2004, Machine Learning.

[9]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[10]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[11]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[12]  Inés María Galván,et al.  Lazy Learning in Radial Basis Neural Networks: A Way of Achieving More Accurate Models , 2004, Neural Processing Letters.

[13]  Hans-Paul Schwefely,et al.  Evolutionary Algorithms: Some Very Old Strategies for Optimization and Adaptation , 1992 .

[14]  Cezary Z. Janikow,et al.  A knowledge-intensive genetic algorithm for supervised learning , 1993, Machine Learning.

[15]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[16]  Inés María Galván,et al.  Multi-step Learning Rule for Recurrent Neural Models: An Application to Time Series Forecasting , 2004, Neural Processing Letters.

[17]  Fernanda Strozzi,et al.  Forecasting high waters at Venice Lagoon using chaotic time series analysis and nonlinear neural networks , 2000 .

[18]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[19]  John C. Platt.  A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.