Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/479
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dc.contributor.authorAryal, S-
dc.contributor.authorNadarajah, D-
dc.contributor.authorKasthurirathna, D-
dc.contributor.authorRupasinghe, L-
dc.contributor.authorJayawardena, C-
dc.date.accessioned2022-01-06T06:01:12Z-
dc.date.available2022-01-06T06:01:12Z-
dc.date.issued2019-12-05-
dc.identifier.citationCited by 3en_US
dc.identifier.isbn978-1-7281-4170-1-
dc.identifier.urihttp://localhost:80/handle/123456789/479-
dc.description.abstractForecasting 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.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2019 International Conference on Advancements in Computing (ICAC);Pages 329-333-
dc.subjectDeep Learningen_US
dc.subjectTime Series Forecastingen_US
dc.subjectTime series analysisen_US
dc.subjectPredictive modelsen_US
dc.subjectComputer architectureen_US
dc.subjectMachine learningen_US
dc.subjectBiological system modelingen_US
dc.subjectData modelsen_US
dc.titleComparative analysis of the application of Deep Learning techniques for Forex Rate predictionen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC49085.2019.9103428en_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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