Time-Series Forecasting of Ethereum Price using Long Short-Term Memory (LSTM) Networks
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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Brac University
2022
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| Dostęp online: | http://hdl.handle.net/10361/17650 |
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10361-176502022-12-14T21:01:39Z Time-Series Forecasting of Ethereum Price using Long Short-Term Memory (LSTM) Networks Al-Wasee, Mohammad Samin Kundu, Promee Shankar Mahzabeen, Israt Tamim, Tasnim Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Cryptocurrency Deep Learning Ether Ethereum Forecasting Long Short-term Memory Multivariate Price Prediction Recurrent Neural Network Time-series Univariate Data mining Time-series analysis--Data processing. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 45-47). In recent times, ether (ETH) has become one of the most popular cryptocurrencies that is gaining significant interest from crypto investors and developers across the globe. The increased interest in this cryptocurrency is due to the fact that transac tions on the Ethereum platform are far more secure, as it combines smart contracts to streamline commerce and trade between both anonymous and recognized parties. Besides, many decentralized financial and nonfinancial apps (DeFi and DApps) are built mainly based on the ether cryptocurrency itself. As a result, the price of this cryptocurrency is also rising gradually. On the other hand, the price of ether some times decreases as well due to some unwanted circumstances like political conflicts, wars, natural disasters, and so on. Thus, the ether cryptocurrency market has be come very unpredictable and can cause an uncertain situation for market investors. For this purpose, having a specialized prediction method for the ether price based on machine learning and deep learning technologies is crucial. This research aims to find an accurate price prediction model for the ether cryptocurrency based on the long short-term memory (LSTM) network, which is a special variant of the recur rent neural network (RNN). In the proposed model, ether price data was taken in time-series format and fitted into multiple basic and hybrid variants of the LSTM network, and the future prices were predicted based on both univariate and mul tivariate time-series analysis. Furthermore, a comparative analysis was conducted among the models and also some popular existing forecasting techniques like autore gressive integrated moving average (ARIMA) as the baseline forecast to determine which one can provide the best possible accuracy so that investors may understand the behaviour of the ether market and make proper decisions on their investment. Mohammad Samin-Al-Wasee Promee Shankar Kundu Israt Mahzabeen Tasnim Tamim B. Computer Science and Engineering 2022-12-14T08:33:33Z 2022-12-14T08:33:33Z 2022 2022-05 Thesis ID: 18101578 ID: 18301295 ID: 18101676 ID: 17301026 http://hdl.handle.net/10361/17650 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 47 Pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Cryptocurrency Deep Learning Ether Ethereum Forecasting Long Short-term Memory Multivariate Price Prediction Recurrent Neural Network Time-series Univariate Data mining Time-series analysis--Data processing. |
| spellingShingle |
Cryptocurrency Deep Learning Ether Ethereum Forecasting Long Short-term Memory Multivariate Price Prediction Recurrent Neural Network Time-series Univariate Data mining Time-series analysis--Data processing. Al-Wasee, Mohammad Samin Kundu, Promee Shankar Mahzabeen, Israt Tamim, Tasnim Time-Series Forecasting of Ethereum Price using Long Short-Term Memory (LSTM) Networks |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Al-Wasee, Mohammad Samin Kundu, Promee Shankar Mahzabeen, Israt Tamim, Tasnim |
| format |
Thesis |
| author |
Al-Wasee, Mohammad Samin Kundu, Promee Shankar Mahzabeen, Israt Tamim, Tasnim |
| author_sort |
Al-Wasee, Mohammad Samin |
| title |
Time-Series Forecasting of Ethereum Price using Long Short-Term Memory (LSTM) Networks |
| title_short |
Time-Series Forecasting of Ethereum Price using Long Short-Term Memory (LSTM) Networks |
| title_full |
Time-Series Forecasting of Ethereum Price using Long Short-Term Memory (LSTM) Networks |
| title_fullStr |
Time-Series Forecasting of Ethereum Price using Long Short-Term Memory (LSTM) Networks |
| title_full_unstemmed |
Time-Series Forecasting of Ethereum Price using Long Short-Term Memory (LSTM) Networks |
| title_sort |
time-series forecasting of ethereum price using long short-term memory (lstm) networks |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17650 |
| work_keys_str_mv |
AT alwaseemohammadsamin timeseriesforecastingofethereumpriceusinglongshorttermmemorylstmnetworks AT kundupromeeshankar timeseriesforecastingofethereumpriceusinglongshorttermmemorylstmnetworks AT mahzabeenisrat timeseriesforecastingofethereumpriceusinglongshorttermmemorylstmnetworks AT tamimtasnim timeseriesforecastingofethereumpriceusinglongshorttermmemorylstmnetworks |
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