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@InCollection{GeumYongLee:1999:aigp3, author = "Geum Yong Lee", title = "Genetic Recursive Regression for Modeling and Forecasting Real-World Chaotic Time Series", booktitle = "Advances in Genetic Programming 3", publisher = "MIT Press", year = "1999", editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline", chapter = "17", pages = "401--423", address = "Cambridge, MA, USA", month = jun, keywords = "genetic algorithms, genetic programming", ISBN = "0-262-19423-6", URL = "http://www.cs.bham.ac.uk/~wbl/aigp3/ch17.pdf", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.141.1197", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1197", abstract = "I explore several extensions to genetic programming for applications involving the forecasting of real world chaotic time series. We first used Genetic Symbolic Regression (GSR),which is the standard genetic programming technique applied to the forecasting problem in the same way that it is often applied to symbolic regression problems [ Koza 1992, 1994]. We observed that the performance of GSR depends on the characteristics of the time series, and in particular that it worked better for deterministic time series than it did for stochastic or volatile time series. Taking a hint from this observation, an assumption was made in this study that the dynamics of a time series comprise a deterministic and a stochastic part. By subtracting the model built by GSR for the deterministic part from the original time series, the stochastic part would be obtained as a residual time series. This study noted the possibility that GSR could be used recursively to model the residual time series of rather stochastic dynamics, which may still comprise another deterministic and stochastic part. An algorithm called GRR (Genetic Recursive Regression) has been developed to apply GSR recursively to the sequence of residual time series of stochastic dynamics, giving birth to a sequence of sub-models for deterministic dynamics extractable at each recursive application. At each recursive application and after some termination conditions are met, the submodels become the basis functions for a series-expansion type representation of a model. The numerical coefficients of the model are calculated by the least square method with respect to the predetermined region of the time series data set. When the region includes the latest data set, the model reflects the most recent changes in the dynamics of a time series, thus increasing the forecasting performance. This chapter shows how GRR has been successfully applied to many real world chaotic time series. The results are compared with those from other GSR-like methods and various soft-computing technologies such as neural networks. The results show that GRR saves much computational effort while achieving enhanced forecasting performance for several selected problems.", notes = "AiGP3", size = "23 pages", }

Genetic Programming entries for Geum Yong Lee