PREDICTING USD/ TL EXCHANGE RATE IN TURKEY: THE LONG-SHORT TERM MEMORY APPROACH - Abdul-razak Bawa Yussif; Fatih Mangır; Zeynep Karaçor; Ayten Yağmur (Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi)
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 USD/TL 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.