Time Series Forecast with Anticipation Using Genetic Programming

  • Daniel Rivero
  • Juan R. Rabuñal
  • Julián Dorado
  • Alejandro Pazos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3512)


This paper presents and application of Genetic Programming (GP) for time series forecast. Although this kind of application has been carried out with a wide range of techniques and with very good results, this paper presents a different approach. In most of the experiments done in time series forecasting the objective is, from a consecutive set of samples or time interval, to obtain the value of the sample in the next time step. The aim of this paper is to study the forecasting not only on the next sample, but in general several samples forward. This will allow the building of more complete prediction systems. With this objective, one of the most widely used series for this kind of application has been used, the Mackey-Glass series.


Genetic Programming Finite Impulse Response Time Series Forecast Chaotic Time Series Genetic Programming Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Danilov, D.L.: Principal Components in Time Series Forecast. Journal of Computational and Graphical Statistics 6, 112–121 (1997)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Jenkins, G.M., Box, G.E.P.: Time Series Analysis Forecasting and Control. Holden Day Edn., Oakland (1976)Google Scholar
  3. 3.
    Casdagli, M.: Nonlinear Prediction of Chaotic Time Series. Physica 35d, 335–356 (1989)MATHMathSciNetGoogle Scholar
  4. 4.
    Kaboudan, M.: Forecasting Demand for Natural Gas Using GP-Econometric Integrated Systems. In: Computing in Economics and Finance 2003. Society for Computational Economics (2003)Google Scholar
  5. 5.
    Dorado, J., Rabuñal, J.R., Puertas, J., Santos, A., Rivero, D.: Prediction and Modelling of the Flow of a Typical Urban Basin Through Genetic Programming. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol. 2279, p. 190. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Rabuñal, J.R., Dorado, J., Puertas, J., Pazos, A., Santos, A., Rivero, D.: Prediction and Modelling of the Rainfall-Runoff Transformation of a Typical Urban Basin using ANN and GP. Applied Artificial Intelligence (2003)Google Scholar
  7. 7.
    Svarer, C.: Designer Networks for Time Series Processing. Neural Networks for Signal Processing III (1993)Google Scholar
  8. 8.
    Dorado, J., Rabuñal, J.R., Santos, A., Pazos, A., Rivero, D.: Automatic Recurrent and Feed-Forward ANN Rule and Expression Extraction with Genetic Programming. Parallel Problem Solving from Nature. In: Proceedings of PPSN VII (2002)Google Scholar
  9. 9.
    Wan, E.A.: Finite Impulse Response Neural Networks with Applications in Time Series Prediction. PhD thesis. University of Stanford (1993)Google Scholar
  10. 10.
    Packard, N.H.: A Genetic Learning Algorithm for the Analysis of Complex Data. Complex Systems 4, 543 (1990)MATHMathSciNetGoogle Scholar
  11. 11.
    Koza, J.: Genetic Programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  12. 12.
    Bennet, H., Koza, J.R., Andre, D., Keane, M.A.: Evolution of a 60 Decibel op amp using Genetic Programming. In: Proceedings of International Conference on Evolvable Systems: From Biology to Hardware (ICES 1996) (1996)Google Scholar
  13. 13.
    Luke, S., Spector, L.: A Revised Comparison of Crossover and Mutation in Genetic Programming. In: 3rd Annual Conference on Genetic Programming. Morgan Kaufmann, San Francisco (1998)Google Scholar
  14. 14.
    Dracopoulos, D.C., Kent, S.: Genetic Programming for Prediction and Control. Neural Computing and Applications 6, 214–228 (1997)CrossRefGoogle Scholar
  15. 15.
    Mackey, M., Glass, L.: Oscillation and chaos in physiological control systems. Science, 197-287 (1977)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Daniel Rivero
    • 1
  • Juan R. Rabuñal
    • 1
  • Julián Dorado
    • 1
  • Alejandro Pazos
    • 1
  1. 1.Fac. InformaticaUniv. A CoruñaA CoruñaSpain

Personalised recommendations