On the Selection of Subset Bilinear Time Series Models: a Genetic Algorithm Approach

Cathy W. S. Chen (), Tsai-Hung Cherng and Berlin Wu
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Tsai-Hung Cherng: Feng Chia University
Berlin Wu: National Chengchi University

Computational Statistics, 2001, vol. 16, issue 4, No 2, 505-517

Abstract: 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.

Keywords: Bilinear time series; genetic algorithm; adaptive function; model selection (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s180-001-8327-9

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