Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Danilov, D.L.: Principal Components in Time Series Forecast. Journal of Computational and Graphical Statistics 6, 112–121 (1997)
Jenkins, G.M., Box, G.E.P.: Time Series Analysis Forecasting and Control. Holden Day Edn., Oakland (1976)
Casdagli, M.: Nonlinear Prediction of Chaotic Time Series. Physica 35d, 335–356 (1989)
Kaboudan, M.: Forecasting Demand for Natural Gas Using GP-Econometric Integrated Systems. In: Computing in Economics and Finance 2003. Society for Computational Economics (2003)
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)
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)
Svarer, C.: Designer Networks for Time Series Processing. Neural Networks for Signal Processing III (1993)
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)
Wan, E.A.: Finite Impulse Response Neural Networks with Applications in Time Series Prediction. PhD thesis. University of Stanford (1993)
Packard, N.H.: A Genetic Learning Algorithm for the Analysis of Complex Data. Complex Systems 4, 543 (1990)
Koza, J.: Genetic Programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)
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)
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)
Dracopoulos, D.C., Kent, S.: Genetic Programming for Prediction and Control. Neural Computing and Applications 6, 214–228 (1997)
Mackey, M., Glass, L.: Oscillation and chaos in physiological control systems. Science, 197-287 (1977)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rivero, D., Rabuñal, J.R., Dorado, J., Pazos, A. (2005). Time Series Forecast with Anticipation Using Genetic Programming. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_119
Download citation
DOI: https://doi.org/10.1007/11494669_119
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
eBook Packages: Computer ScienceComputer Science (R0)