Kendinden Uyarımlı Eşik Otoregresif Modellerin Belirlenmesi İçin Genetik Algoritma Yaklaşımı

Year 2017, Volume: 5 Issue: 2, 319 - 332, 01.04.2017

Serkan Taştan Nilgün Çil

Abstract

Günümüzde doğrusal olmayan zaman serisi analizinde yaygın olarak kullanılan kendinden uyarımlı eşik otoregresif SETAR modeller; anlaşılması ve yorumlanması kolay, basit bir model biçimine sahip olsalar da söz konusu modeller belirlenirken tahmin edilmesi gereken birçok serbest parametre bulunmaktadır. Bu nedenle bu çalışmada SETAR modellerini belirleme süreci bir optimizasyon problemi olarak düşünülmüş ve ilgili probleme genetik algoritmalar ile çözüm aranmıştır. Bu bağlamda ele alınan probleme ilişkin genetik algoritma bileşenleri tanımlanmış ve algoritma için uygun parametreler belirlenmiştir. Önerilen yaklaşım simülasyon verileri ile değerlendirilerek, kullanılabilirliği gösterilmiştir.

Keywords

Kendinden Uyarımlı Eşik Otoregresif, SETAR, Genetik Algoritma, Model Belirleme

A Genetic Algorithm Approach for the Specification of Self-Exciting Threshold Autoregressive Models

Abstract

Even though, self-exciting threshold autoregressive SETAR models commonly used in nonlinear time series analysis nowadays have a simple, easy to understand and interpret model form, there are many free parameters to estimate, when building the models in question. Therefore in this paper the process of specification of SETAR models were considered as an optimization problem and the related problem was solved with genetic algorithms. In this context genetic algorithm components were defined for the discussed problem and appropriate parameters were determined for the algorithm. The usability of the proposed approach is shown by evaluating it with simulation data.

Keywords

Self-Exciting Threshold Autoregressive, SETAR, Genetic Algorithms, Model Specification

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