Summary
This paper explores the idea of using a Genetic algorithm (GA) to solve the problem of subset model selection within the class of bilinear time series processes. The research is based on the concept of evolution theory as well as that of survival of the fittest. We use the AIC, BIG or SBC criteria as the adaptive functions to measure the degree of fitness. During the GA process, the best-fitted population is selected and certain characteristics are translated into the next generation. Simulation results demonstrate that genetic-based learning can effectively work out a pattern of the underlying time series. Finally, we illustrate how the GA can be applied successfully to subset selection in a bilinear time series via several examples and a simulation study.
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Acknowledgments
The authors would like to thank the anonymous referee for comments and suggestions which greatly improved this paper. This research is supported by grants (NSC 87-2118-M-035-004, NSC 88-2118-M-035-001) from National Science Council of Taiwan for which Chen, C.W.S. and Cherng, T.-H. are grateful.
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Chen, C.W.S., Cherng, TH. & Wu, B. On the Selection of Subset Bilinear Time Series Models: a Genetic Algorithm Approach. Computational Statistics 16, 505–517 (2001). https://doi.org/10.1007/s180-001-8327-9
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DOI: https://doi.org/10.1007/s180-001-8327-9