Please use this identifier to cite or link to this item: http://ir.lib.seu.ac.lk/handle/123456789/6402
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFaathima Fayaza, M. S.-
dc.contributor.authorRaheem, Fanoon-
dc.contributor.authorIqbal, Nihla-
dc.date.accessioned2023-01-05T05:30:17Z-
dc.date.available2023-01-05T05:30:17Z-
dc.date.issued2021-12-30-
dc.identifier.citationSri Lankan Journal of Technology (SLJoT), 2(2); pp. 27-31.en_US
dc.identifier.issn2773-6970-
dc.identifier.urihttp://ir.lib.seu.ac.lk/handle/123456789/6402-
dc.description.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.en_US
dc.language.isoen_USen_US
dc.publisherFaculty of Technology, South Eastern University of Sri Lankaen_US
dc.subjectDeep learningen_US
dc.subjectFinancial time series forecastingen_US
dc.subjectRecurrent neural networksen_US
dc.subjectForeign exchange rateen_US
dc.titlePrediction of forex rate using deep learning: us dollar to Sri Lankan rupeesen_US
dc.typeArticleen_US
Appears in Collections:Volume 02 Issue 2

Files in This Item:
File Description SizeFormat 
SLJoT_2021_02_005.pdf314 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.