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DC Field | Value | Language |
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dc.contributor.author | Aryal, S | - |
dc.contributor.author | Nadarajah, D | - |
dc.contributor.author | Kasthurirathna, D | - |
dc.contributor.author | Rupasinghe, L | - |
dc.contributor.author | Jayawardena, C | - |
dc.date.accessioned | 2022-01-06T06:01:12Z | - |
dc.date.available | 2022-01-06T06:01:12Z | - |
dc.date.issued | 2019-12-05 | - |
dc.identifier.citation | Cited by 3 | en_US |
dc.identifier.isbn | 978-1-7281-4170-1 | - |
dc.identifier.uri | http://localhost:80/handle/123456789/479 | - |
dc.description.abstract | Forecasting the financial time series is an extensive field of study. Even though the econometric models, traditional machine learning models, artificial neural networks and deep learning models have been used to predict the financial time series, deep learning models have been recently employed to do predictions of financial time series. In this paper, three different deep learning models called Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Temporal Convolution Network (TCN) have been used to predict the United States Dollar (USD) to Sri Lankan Rupees (LKR) exchange rate and compared the accuracy of the models. The results indicate the superiority of CNN model over other models. We conclude that CNN based models perform best in financial time series prediction. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartofseries | 2019 International Conference on Advancements in Computing (ICAC);Pages 329-333 | - |
dc.subject | Deep Learning | en_US |
dc.subject | Time Series Forecasting | en_US |
dc.subject | Time series analysis | en_US |
dc.subject | Predictive models | en_US |
dc.subject | Computer architecture | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Biological system modeling | en_US |
dc.subject | Data models | en_US |
dc.title | Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ICAC49085.2019.9103428 | en_US |
Appears in Collections: | 1st International Conference on Advancements in Computing (ICAC) | 2019 Research Papers - Dept of Computer Science and Software Engineering Research Papers - IEEE Research Papers - IEEE |
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File | Description | Size | Format | |
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Comparative_analysis_of_the_application_of_Deep_Learning_techniques_for_Forex_Rate_prediction.pdf Until 2050-12-31 | 1.05 MB | Adobe PDF | View/Open Request a copy |
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