Affiliation:
1. AKDENİZ ÜNİVERSİTESİ
2. SELÇUK ÜNİVERSİTESİ
3. University of Manitoba
Abstract
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.
Publisher
Mehmet Akif Ersoy Universitesi Iktisadi ve Idari Bilimler Fakultesi Dergisi
Subject
Organic Chemistry,Biochemistry
Reference22 articles.
1. Azzouni, A. and Pujolle, G. (2017), “A Long Short-Term Memory Recurrent Neural Network Framework For Network Traffic Matrix Prediction”, arXiv preprint arXiv:1705.05690 (Accessed: 23.02.2021).
2. Bengio, Y., Simard, P. and Frasconi, P. (1994), “Learning Long-Term Dependencies With Gradient Descent İs Difficult”, Ieee Transactions On Neural Networks, Vol. 5, No. 2: 157-166.
3. Chandwani, D., and Manminder S.S. (2014), “Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System With Market İndicators İn The Indian Context”, International Journal of Computer Applications, Vol. 92, No. 11: 8-17.
4. Das, S.R., Mishra, D. and Rout, M. (2020), “A Hybridized ELM-Jaya Forecasting Model For Currency Exchange Prediction”, Journal of King Saud University-Computer and Information Sciences, Vol. 32, No:3: 345-366.
5. Faraway, J., and Chatfield, C. (1998), “Time Series Forecasting With Neural Networks: A Comparative Study Using The Airline Data”, Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 47, No. 2: 231-250.