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Title: Prediction of forex rate using deep learning: us dollar to Sri Lankan rupees
Authors: Faathima Fayaza, M. S.
Raheem, Fanoon
Iqbal, Nihla
Keywords: Deep learning
Financial time series forecasting
Recurrent neural networks
Foreign exchange rate
Issue Date: 30-Dec-2021
Publisher: Faculty of Technology, South Eastern University of Sri Lanka
Citation: Sri Lankan Journal of Technology (SLJoT), 2(2); pp. 27-31.
Abstract: —Exchange rate forecasting is a vital problem in the economic aspect of every country in the world. Prediction of the foreign exchange rate is a very complex and challenging task. A more in-depth analysis and forecasting techniques assist the traders in good decision-making in their commercial activities. This paper discusses forecasting of USD to LKR foreign exchange rate using Artificial Neural Network (ANN) and Recurrent Neural Networks (RNN). This study used two variant Recurrent Neural Networks, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Rectified Linear Unit (ReLU) is used as an activation function. Adam and Stochastic Gradient Descent (SGD) are used as the optimizers in this research. The study mainly compares the performance of ANN, LSTM, and GRU prediction rates with two different optimizers Adam and SDG. Mean Square Error (MSE) is used as the loss function. The study finds that GRU with Adam optimizer performs better than other approaches in terms of R2 squared (Coefficient of determination), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE). In contrast, LSTM performs better with SDG optimizer when compared to Adam.
ISSN: 2773-6970
Appears in Collections:Volume 02 Issue 2

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