Design of Neural Networks for Time Series Prediction Using Case-Initialized Genetic Algorithms

One of the major objectives of time series analysis is the design of time series models, used to support the decision-making in several application domains. Among the existing time series models, we highlight the Artificial Neural Networks (ANNs), which offer greater computational power than the classical linear models. However, as a drawback, the performance of ANNs is more vulnerable to wrong design decisions. One of the main difficulties of ANN’s design is the selection of an adequate network’s architecture. In this work, we propose the use of Case-Initialized Genetic Algorithms to help in the ANN’s design. We maintain a case base in which each case associates a time series to a wellsucceeded neural network used to predict it. Given a new time series, the most similar cases are retrieved and their solutions are inserted in the initial population of the Genetic Algorithms (GAs). Next, the GAs are executed and the best generated neural model is returned. In the undergone tests, the Case-Initialized GAs presented a better generalization performance than the GAs with random initialization. We expect that the results will be improved as more cases are inserted

[1]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[2]  John Moody,et al.  Prediction Risk and Architecture Selection for Neural Networks , 1994 .

[3]  Jari Kyngas,et al.  An evolutionary approach to neural network design applied to sunspot prediction , 1996 .

[4]  Lennart Ljung,et al.  Neural Networks in System Identification , 1994 .

[5]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[6]  Xin Yao,et al.  Evolutionary Artificial Neural Networks , 1993, Int. J. Neural Syst..

[7]  Zoran Obradovic,et al.  Efficient design of neural networks for time series prediction , 1997 .

[8]  Sushil J. Louis,et al.  Robustness of Case-Initialized Genetic Algorithms , 1999, FLAIRS Conference.

[9]  Vasant Honavar,et al.  Evolutionary Design of Neural Architectures , 1995 .

[10]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[11]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  Les Oakshott.  Forecasting: Time Series Analysis , 2009 .

[14]  P. Cartwright.  FORECASTING TIME SERIES:A COMPARATIVE ANALYSIS OF ALTERNATIVE CLASSES OF TIME SERIES MODELS , 1985 .

[15]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[16]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[17]  Georg Dorffner,et al.  Neural Networks for Time Series Processing , 1996 .

[18]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[19]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .