Abstract
Time Series Forecasting (TSF) allows the modeling of complex systems as “black-boxes”, being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binary EA will search for the best ARMA model, being the parameters optimized by a (low-level) EA, which encodes real values. The handicap of this approach is compared with conventional forecasting methods, being competitive.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agapie, A. and A. Agapie. (1997). “Forecasting the Economic Cycles Based on an Extension of the Holt-Winters Model. A Genetic Algorithms Approach.” In Proc.of the IEEE Int.Conf.On Computational Intelligence for Financial Forecasting Engineering (CIFEr'97),New York, pp. 96–99.
Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press.
Box, G. and G. Jenkins. (1976). Time Series Analysis: Forecasting and Control. Holden Day, San Francisco, USA.
Cortez, P., M. Rocha, and J. Neves. (2001). “Genetic and Evolutionary Algorithms for Time Series Forecasting.” In Monostori, L., Váncza, J., and Alis, M. (eds.), Engineering of Intelligent Systems: Proc.of IEA/AIE 2001, LNAI vol. 2070, Springer, pp. 393–402.
Faraday, J. and C. Chatfield. (1998). “Times Series Forecasting with Neural Networks: A Case Study.” Applied Statistics 47, 231–250.
Flexer, A. (1996). “Statistical Evaluation of Neural Networks Experiments: Minimum Requirements and Current Practice.” In Proc.of the 13th European Meeting on Cybernetics and Systems Research,vol. 2, pp. 1005–1008.
Fogel, L. (1999). Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming.New York: John Wiley.
Grenfenstette, J. (1986). “Optimization of Control Parameters for Genetic Algorithms.” IEEE Trans.on Systems, Man, and Cybernetics 16(1), 122–128.
Huang, C. and H. Yang. (1995). “A Time Series Approach To Short Term Load Forecasting Through Evolutionary Programming Structures.” In Proc.oftheEMPD'95 Int.Conf.,pp583–588.
Hyndman, R. (2003). Time Series Data Library.Available from http://www-personal.buseco.monash.edu. au /~hyndman/TSDL/.
Makridakis, S., S. Weelwright, and R. Hyndman. (1998). Forecasting: Methods and Applications, 3rd edn. New York, USA: John Wiley & Sons.
Michalewicz, Z. (1996). Genetic Algorithms +Data Structures =Evolution Programs, 3rd edn. USA: Springer-Verlag.
Neves, J., M. Rocha, H. Rodrigues, M. Biscaia, and J. Alves. (1999). “Adaptive Strategies and the Design of Evo-lutionary Applications.” In Banzhaf, W., Daida, J., Eiben, A., Garzon, M., Honavar, V., Jakiela, M., and Smith, R. (eds.), Proc.of the Genetic and Evolutionary Computation Conference (GECCO99). Morgan Kaufmann Publishers, pp. 473–479.
Rolf, S., J. Sprave, and W. Urfer. (1997). “Model Identification and Parameter Estimation of ARMA Models by Means of Evolutionary Algorithms.” In Proc.of IEEE/IAFE Conf.Computational Intelligence for Financial Engineering (CIFEr'97), pp. 237–243.
Sarle, W. (1995). “Stopped Training and Other Remedies for Overfitting.” In Proc.of the 27th Symp.on the Interface of Computer Science and Statistics, pp. 352–360.
Schwarz, G. (1978). “Estimating the Dimension of a Model.” The Annals of Statistics 6, 461–464.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Cortez, P., Rocha, M. & Neves, J. Evolving Time Series Forecasting ARMA Models. Journal of Heuristics 10, 415–429 (2004). https://doi.org/10.1023/B:HEUR.0000034714.09838.1e
Issue Date:
DOI: https://doi.org/10.1023/B:HEUR.0000034714.09838.1e