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ZAMAN SERİSİANALİZİNDE MLP YAPAY SİNİR AĞLARI VE ARIMA MODELİNİN KARŞILAŞTIRILMASI

2009

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Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi

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Bu çalışmada zaman serisi analizinde yaygın olarak kullanılan Box-Jenkis modelleri ile ileri beslemeli yapay sinir ağlarının bir karşılaştırması yapılmıştır. Veri seti olarak aylık ve günlük döviz (YTL/$) kuru verileri kullanılmıştır. Farklı Box-Jenkins ve yapay sinir ağları modelleri oluşturulmuş, her bir teknik içiniyi sonuçları veren modeller seçilerek karşılaştırma yapılmış-tır. Elde edilen sonuçlar Yapay sinir ağlarının finansal verilerin tahmininde kullanılabilecek başarılı bir yöntem olduğunu göstermiştir

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Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi

Field : Sosyal, Beşeri ve İdari Bilimler

Journal Type : Ulusal

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Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi

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ZAMAN SERİSİANALİZİNDE MLP YAPAY SİNİR AĞLARI VE ARIMA MODELİNİN KARŞILAŞTIRILMASI

2009

Journal:

Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi

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To view citations of publications, you must access Sobiad from a Member University Network. You can contact the Library and Documentation Department for our institution to become a member of Sobiad.

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Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi

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User Guide

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Mendeley

Endnote

ZAMAN SERİSİANALİZİNDE MLP YAPAY SİNİR AĞLARI VE ARIMA MODELİNİN KARŞILAŞTIRILMASI

2009

Journal:

Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi

Author:

Abstract:

Keywords:

Citation Owners

Attention! To view citations of publications, you must access Sobiad from a Member University Network. You can contact the Library and Documentation Department for our institution to become a member of Sobiad.

Off-Campus AccessIf you are affiliated with a Sobiad Subscriber organization, you can use Login Panel for external access. You can easily sign up and log in with your corporate e-mail address.

Similar Articles

Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi

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Attention!
To view citations of publications, you must access Sobiad from a Member University Network. You can contact the Library and Documentation Department for our institution to become a member of Sobiad.

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If you are affiliated with a Sobiad Subscriber organization, you can use Login Panel for external access. You can easily sign up and log in with your corporate e-mail address.