Faculty of Computing

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Author: search.filters.author.Jayawardena, CSubject: search.filters.subject.Deep Learning

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Now showing 1 - 3 of 3
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    Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction
    (IEEE, 2019-12-05) Aryal, S; Nadarajah, D; Kasthurirathna, D; Rupasinghe, L; Jayawardena, C
    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.
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    Smart Mirror with Virtual Twin
    (IEEE, 2019-12-05) Abeydeera, S. S; Bandaranayake, M; Karunarathna, H. U; Pallewatta, S; Dharmasiri, P; Gunathilake, B; Saparamadu, S; Senanayake, B; Jayawardena, C
    Smart Mirror with a virtual twin who helps the user as a close companion. The virtual twin monitors the user's physical appearance and tracks the data gathered from given inputs. Since this is an intelligent virtual twin it uses machine learning techniques. It helps to improve the user's mental and physical health by detecting medical conditions and providing suitable suggestions in a more personalized way. This virtual twin not only focuses on physical or mental health conditions but also gives friendly suggestions about suitable styles which helps to improve the person's life quality.
  • Thumbnail Image
    PublicationEmbargo
    Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction
    (IEEE, 2019-12-05) Aryal, S; Nadarajah, D; Kasthurirathna, D; Rupasinghe, L; Jayawardena, C
    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.