Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/18839


Title: Using Genetic Algorithms to Parameters (d r) Estimation for Threshold Autoregressive Models
Authors: Wu, Berlin
吳柏林
Chung, Chih-Li
Keywords: Genetic algorithms;Threshold autoregressive models;Fitness function;Exchange rate
Date: 2002-01
Issue Date: 2008-12-24 13:38:51 (UTC+8)
Abstract: Threshold autoregressive model (TAR model) has certain characteristics due to which linear models fail to fit a nonlinear time series, while the problem of how to find an appropriate threshold value still attracts many researchers’ attention. In this paper, we apply the genetic algorithms to estimate the threshold and lag parameters r and d for TAR models. The selection operator is formulated following Darwin's principle of survival of the fittest to guide the trek through a search space. The crossover and mutation operators have been inspired by the mechanisms of gene mutation and chromosome recombination.
Relation: Computational Statistics and Data Analysis,38(3),315-330
Data Type: article
DOI 連結: http://dx.doi.org/10.1016/S0167-9473(01)00030-5
Appears in Collections:[應用數學系] 期刊論文

Files in This Item:

File Description SizeFormat
315-330.pdf176KbAdobe PDF508View/Open


All items in 學術集成 are protected by copyright, with all rights reserved.


社群 sharing