%0 Conference Paper %1 lee:2001:tspgp %A Lee, G. Y. %B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001 %C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea %D 2001 %I IEEE Press %K Perturbation Series, Theory Time algorithms, genetic programming, %P 403--409 %T Time Series Perturbation by Genetic Programming %X We present a new algorithm that combines perturbation theory and genetic programming for modelling and forecasting real-world chaotic time series. Both perturbation theory and time series modeling have to build symbolic models for very complex system dynamics. Perturbation theory does not work without a well-defined system equation. Difficulties in modelling time series lie in the fact that we cannot have or assume any system equation. The new algorithm shows how genetic programming can be combined with perturbation theory for time series modelling. Detailed discussions on successful applications to chaotic time series from practically important fields of science and engineering are given. Computational resources were negligible as compared with earlier similar regression studies based on genetic programming. A desktop PC provides sufficient computing power to make the new algorithm very useful for real-world chaotic time series. Especially, it worked very well for deterministic or stationary time series, while stochastic or nonstationary time series needed extended effort, as it should be %@ 0-7803-6658-1

@inproceedings{lee:2001:tspgp, abstract = {We present a new algorithm that combines perturbation theory and genetic programming for modelling and forecasting real-world chaotic time series. Both perturbation theory and time series modeling have to build symbolic models for very complex system dynamics. Perturbation theory does not work without a well-defined system equation. Difficulties in modelling time series lie in the fact that we cannot have or assume any system equation. The new algorithm shows how genetic programming can be combined with perturbation theory for time series modelling. Detailed discussions on successful applications to chaotic time series from practically important fields of science and engineering are given. Computational resources were negligible as compared with earlier similar regression studies based on genetic programming. A desktop PC provides sufficient computing power to make the new algorithm very useful for real-world chaotic time series. Especially, it worked very well for deterministic or stationary time series, while stochastic or nonstationary time series needed extended effort, as it should be}, added-at = {2008-06-19T17:35:00.000+0200}, address = {COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea}, author = {Lee, G. Y.}, biburl = {https://www.bibsonomy.org/bibtex/218586207e643ed65eede5d7ff4d80af7/brazovayeye}, booktitle = {Proceedings of the 2001 Congress on Evolutionary Computation CEC2001}, interhash = {9b647dbdf9e75c17ec88234bc409da1f}, intrahash = {18586207e643ed65eede5d7ff4d80af7}, isbn = {0-7803-6658-1}, keywords = {Perturbation Series, Theory Time algorithms, genetic programming,}, month = {27-30 May}, notes = {CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =}, organisation = {IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)}, pages = {403--409}, publisher = {IEEE Press}, publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA}, timestamp = {2008-06-19T17:45:17.000+0200}, title = {Time Series Perturbation by Genetic Programming}, year = 2001 }