Summary
In this paper a system based on Genetic Programming for forecasting nonlinear time series is outlined. Our system is endowed with two features. Firstly, at any given time t, it performs a τ-steps ahead prediction (i.e. it forecasts the value at time t + τ) based on the set of input values for the n time steps preceding t. Secondly, the system automatically finds among the past n input variables the most useful ones to estimate future values. The effectiveness of our approach is evaluated on El Niño 3.4 time series on the basis of a 12-month-ahead forecast.
Key words
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Weigend A, Gershenfeld N (eds) (1994). Time series prediction: forecasting the future and understanding the past, Addison-Wesley, Reading, Massachussetts.
Tong H (1990). Nonlinear time series: a dynamical system approach. Oxford University Press.
Box G, Jenkins G, Reinsel G (1994). Time series analysis: forecasting and control. Prentice Hall, Englewood Cliffs, New Jersey.
Friedman J (1991). Multivariate adaptive regression splines. Annals of Statistics 19:1–142.
Mozer N (1993). Neural net for temporal sequence processing. In: Weigend A, Gershenfeld N (eds) Time series prediction: forecasting the future and understanding the past. Addison-Wesley, Reading, Massachussetts.
Tsoi A, Back A (1994). Locally recurrent globally feedforward networks: a critical review of architectures. IEEE Trans. on Neural Networks 2:229–239.
Koskela T, Lehtokangas M, Saarinen J, Kaski K (1996). Time series prediction with multilayer perceptron, FIR and Elman neural networks. In: World Congress on Neural Networks, INNS Press, 491–496.
De Falco I, Iazzetta A, Luongo G, Mazzarella A, Tarantino E (2000). The seismicity in the southern tyrrhenian area and its neural forecasting. Pure and Applied Geophysics 157:343–355.
Cybenko G (1989). Approximations by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 2:303–314.
Angeline P J (1998). Evolving predictors for chaotic time series. In: Rogers S, Fogel D, Bezdek J, Bosacchi B (eds) Proceedings of SPIE: Application and Science of Computational Intelligence, Vol. 3390. Bellingham, WA, USA, 170–180.
De Falco I, Della Cioppa A, Iazzetta A, Natale P, Tarantino E (1999). Optimizing neural networks for time series prediction. In: Advances in Soft Computing in Engineering Design and Manufacturing. Springer-Verlag, London.
Lee Ki-Youl, Lee Dong-Wook, Sim Kwee-Bo (2000). Evolutionary neural networks for time series prediction based on L-system and DNA coding method. In: Congress on Evolutionary Computation, La Jolla, California, USA, July 16–19. IEEE Press, 1467–1474.
Koza J (1992). Genetic programming. On the programming of computers by means of natural selection. MIT Press, Cambridge, MA, USA.
Iba H (1993) System identification using structured genetic algorithms. In: Forrest S. (Ed.) Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California. Morgan Kaufmann, 279–286.
Oakley H (1994) Two scientific applications of genetic programming: stack filters and nonlinear equation fitting to chaotic data. In: Kinnear K (ed.) Advances in Genetic Programming. MIT Press, Cambridge, MA, USA, 369–389.
Mulloy B S, Riolo R L, Savit R S (1996). Dynamics of genetic programming and chaotic time series Prediction. In: Koza J R, Goldberg D E, Fogel D B, Riolo R L (eds), Genetic Programming 1996: Proceedings of First Annual Conference, Stanford University, CA, USA, July 28–31, 166–174.
Numata M, Yoshihara I, Yoshizawa M, Abe K (1999). GP-based heart rate prediction for artificial heart control. In: Brave S. and Wu A. S. (Eds) Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, July 13–17, 193–197.
Iba H, Nikolaev N I (2000). Genetic programming polynomial models of financial data series. In: Congress on Evolutionary Computation, La Jolla, California, USA, July 16–19. IEEE Press, 1459–1466.
Nikolaev N I, Iba H (2001) Regularization approach to inductive genetic programming. IEEE Trans. on Evolutionary Computation, 4:359–375.
Duan M, Povinelli R J (2001). Estimating stock price predictability using genetic programming. In: Spector L, Goodman E D, Wu A, Langdon W B, Voigt H M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon M H, Burke E (eds) Genetic and Evolutionary Computation Conference (GECCO-2001), S. Francisco, California, USA, July 7–11. Morgan Kaufmann, 193–197.
Zongker D, Punch W (1995). lilgp version 1.0, Lansing, Michigan State University, GA Research and Application Group. Available at: http://isl.cps.msu.edu/GA/software/lil-gp.
Takens F (1981). Detecting strange attractors in turbulence. In: Lecture Notes in Mathematics. Springer-Verlag, Berlin.
Jäske H (1996). Prediction of sunspots by genetic programming and their applications. In: Alander J T (ed), Proceedings of Second Nordic Workshp on Genetic Algorithms, Vaasa, Finland, 19–23 August, 79–88.
Trenberth K E, (1996) El Niño definition. CLIVAR — Exchanges, 3:6–8.
Rasmusson E M, Carpenter T H (1982) Variations in tropical sea surface temperature and surface wind fields. Associated with the Southern Oscillation/El Niño, Monthly Weather Review, Vol. 110, 354–384.
Trenberth K E (1997), The definition of El Niño. Bullettin of the American Meteorological Soc., Vol. 78, 2771–2777.
von Storch H, Zwiers F W (1999) Statistical Analysis in Climate Research. Cambridge University Press, 484.
El Niño 3.4 SST index series. Available at: http://www.cgd.ucar.edu/cas/catalog/climind/Nino_3_3.4_indices.html.
Barnston A G, Glantz M H, He Y (1998) Predictive skill of statistical and dynamical climate models in SST forecasts during the 1997–98 El Niño episode and the 1998 La Niña onset. Bulletin of the American Meteorological Soc., 217–243.
Unger D, Barnston A, van den Dool H, Kousky V (1996) Consolidated forecasts of tropical pacific SST in Niño 3.4 using one dynamical model and two statistical models. Experimental Long-Lead Bulletin, Vol. 5:1 of the Hardcopy Version.
Hsieh W (2000) Nonlinear canonical correlation analysis of the tropical pacific climate variability using a neural network approach. Journal of Climate, 14:2528–2539.
Tang B, Hsieh W W, Tangang F T (1998) Neural network model forecasts of the Niño 3.4 sea surface temperature. Experimental Long-Lead Forecast Bulletin, Vol. 7:1.
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
De Falco, I., Della Cioppa, A., Tarantino, E. (2005). A Genetic Programming System for Time Series Prediction and Its Application to El Niño Forecast. In: Hoffmann, F., Köppen, M., Klawonn, F., Roy, R. (eds) Soft Computing: Methodologies and Applications. Advances in Soft Computing, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32400-3_12
Download citation
DOI: https://doi.org/10.1007/3-540-32400-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25726-4
Online ISBN: 978-3-540-32400-3
eBook Packages: EngineeringEngineering (R0)