PREDICTING USD/ TL EXCHANGE RATE IN TURKEY: THE LONG-SHORT TERM MEMORY APPROACH

Ayten Yağmur; Zeynep Karaçor; Fatih Mangır; Abdul-razak Bawa Yussif

Makale detayları

PREDICTING USD/ TL EXCHANGE RATE IN TURKEY: THE LONG-SHORT TERM MEMORY APPROACH

Yazarlar:

Ayten Yağmur | Zeynep Karaçor | Fatih Mangır | Abdul-razak Bawa Yussif

Yayıncı:

Burdur Mehmet Akif Ersoy University

Dergi:

Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty Vol.10 Issue 2, pp.935-949

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293744

pISSN:

2149-1658

Anahtar kelimeler:

Özet:

 The prediction of the exchange rate time series has been quite challenging but is an essential process. This is as a result of the inherent noise and the volatile behavior in these series. Time series analysis models such as ARIMA have been used for this purpose. However, these models are limited due to the fact that they are not able to explain the non-linearity as well as the stochastic properties of foreign exchange rates. In order to perform a more accurate exchange rate prediction, deep-learning methods have been employed withremarkable rates of success. In this paper, we apply the Long-Short Term Memory Neural Network to predict the USDTL exchange rate in Turkey. The result from this paper indicates that the Long-Short Term Memory Neural Network deep learning method gives higher prediction accuracy compared to the Auto Regressive Integrated Moving Average and the Multilayer Perception Neural Network models.

 The prediction of the exchange rate time series has been quite challenging but is an essential process. This is as a result of the inherent noise and the volatile behavior in these series. Time series analysis models such as ARIMA have been used for this purpose. However, these models are limited due to the fact that they are not able to explain the non-linearity as well as the stochastic properties of foreign exchange rates. In order to perform a more accurate exchange rate prediction, deep-learning methods have been employed withremarkable rates of success. In this paper, we apply the Long-Short Term Memory Neural Network to predict the USDTL exchange rate in Turkey. The result from this paper indicates that the Long-Short Term Memory Neural Network deep learning method gives higher prediction accuracy compared to the Auto Regressive Integrated Moving Average and the Multilayer Perception Neural Network models.

 The prediction of the exchange rate time series has been quite challenging but is an essential process. This is as a result of the inherent noise and the volatile behavior in these series. Time series analysis models such as ARIMA have been used for this purpose. However, these models are limited due to the fact that they are not able to explain the non-linearity as well as the stochastic properties of foreign exchange rates. In order to perform a more accurate exchange rate prediction, deep-learning methods have been employed withremarkable rates of success. In this paper, we apply the Long-Short Term Memory Neural Network to predict the USDTL exchange rate in Turkey. The result from this paper indicates that the Long-Short Term Memory Neural Network deep learning method gives higher prediction accuracy compared to the Auto Regressive Integrated Moving Average and the Multilayer Perception Neural Network models.

 Döviz kuru zaman serisinin tahmini oldukça zorlu, ancak önemli bir süreçtir. Bu, serilerdeki kalıtsal gürültü özelliğinin ve kırılgan davranışının sonucudur. Bu amaçla ARIMA gibi zaman serisi analiz modelleri kullanılmıştır. Ancak bu modeller döviz kurlarının stokastik özelliklerinin yanı sıra doğrusal olmama özelliklerini de açıklayamamaları nedeniyle sınırlıdırlar. Daha doğru bir döviz kuru tahmini gerçekleştirmek için, önemli başarı oranlarına sahip derin öğrenme yöntemleri uygulanmaktadır. Bu çalışma da, Türkiyedeki USDTL kurunu tahmin etmek için Uzun-Kısa Vadeli Bellek Sinir Ağı yöntemi uygulanmaktadır. Bu makaleden elde edilen sonuç, Uzun-Kısa Süreli Bellek Sinir Ağı derin öğrenme yönteminin otoregresif hareketli ortalamalar yöntemi ile Çok katmanlı Yapay Sinir Ağı modellerine kıyasla daha yüksek tahmin yapmaktadır.

Makale detayları

PREDICTING USD/ TL EXCHANGE RATE IN TURKEY: THE LONG-SHORT TERM MEMORY APPROACH

Yazarlar:

Ayten Yağmur | Zeynep Karaçor | Fatih Mangır | Abdul-razak Bawa Yussif

Yayıncı:

Burdur Mehmet Akif Ersoy University

Dergi:

Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty Vol.10 Issue 2, pp.935-949

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293744

pISSN:

2149-1658

Prediction

Exchange Rate

Time Series

ARIMA

LSTM

MLP.Tahmin

Döviz Kuru

Zaman Serileri

ARIMA

LSTM

MLP.

Sayı

Makale detayları

Yazarlar: Ayten Yağmur | Zeynep Karaçor | Fatih Mangır | Abdul-razak Bawa Yussif

Yayıncı: Burdur Mehmet Akif Ersoy University

Dergi: Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty Vol.10 Issue 2, pp.935-949

Belge Tipi: Makale

Yayın Tarihi: 2/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001293744

pISSN: 2149-1658

Sayı

Yazarlar:

Ayten Yağmur | Zeynep Karaçor | Fatih Mangır | Abdul-razak Bawa Yussif

Yayıncı:

Burdur Mehmet Akif Ersoy University

Dergi:

Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty Vol.10 Issue 2, pp.935-949

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293744

pISSN:

2149-1658