Meiliyani Siringoringo, . and Irhamah, . (2015) ESTIMATION OF LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE MODEL USING GENETIC ALGORITHM. Proceeding of International Conference On Research, Implementation And Education Of Mathematics And Sciences 2015 (ICRIEMS 2015), Yogyakarta State University, 17-19 May 2015. ISSN 978-979-96880-8-8
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Abstract
Time series data not only create linear model but also nonlinear model, especially in the economic. One of nonlinear model in time series is smooth transition autoregressive (STAR). STAR model is the development of Self-Exciting Autoregressive (SETAR) model with two regimes. STAR smoothen the SETAR two regimes with a transition function and one of that is logistic function which then form LSTAR models (logistic smooth transition autoregressive). After, determining order p and delay d, estimation of π1, π2, γ and c using genetic algotihm. By GA (genetic algorithm), that is a technique to search for solutions with various combinations of genes in a chromosome in order to obtain a global optimum solution, . Evaluation of chromosomes carried by the fitness function through procedures such as selection, crossover and mutation. Application of genetic algorithm (GA) will be conducted to identify the data model of LSTAR on stock return are included in the LQ 45 index that show LSTAR with GA better than AR model. Key words: genetic algorithm, nonlinear, stock return, LSTAR
| Item Type: | Article |
|---|---|
| Subjects: | Prosiding > ICRIEMS 2015 > Mathematics & Mathematics Education |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Pendidikan Matematika > Matematika |
| Depositing User: | Administrator |
| Date Deposited: | 15 Jul 2015 21:31 |
| Last Modified: | 15 Jul 2015 21:31 |
| URI: | http://eprints.uny.ac.id/id/eprint/23638 |
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