Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches

Conference paper


Sontakke, P., Jafari, F., Saeedi, M. and Amirhosseini, M. 2024. Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches. ICDAM-2023: 4th International Conference on Data Analytics & Management. London, UK 23 - 24 Jun 2023 Springer. https://doi.org/10.1007/978-981-99-6544-1_7
AuthorsSontakke, P., Jafari, F., Saeedi, M. and Amirhosseini, M.
TypeConference paper
KeywordsCryptocurrency; Bitcoin Price; Time Series Forecasting; Machine Learning; Technical Indicators; Linear Regression; Support Vector Regression; Extreme Gradient Boosting; Long Short-Term Memory
Year2024
ConferenceICDAM-2023: 4th International Conference on Data Analytics & Management
PublisherSpringer
Accepted author manuscript
License
File Access Level
Anyone
Publication dates
Print13 Jan 2024
Publication process dates
Accepted17 Mar 2023
Deposited09 May 2023
Journal citationpp. 81-94
ISSN2367-3370
Book titleProceedings of Data Analytics and Management: ICDAM 2023, Volume 1
Book editorSwaroop, A.
Polkowski, Z.
Correia, S. D.
Virdee, B.
ISBN9789819965434
9789819965441
Digital Object Identifier (DOI)https://doi.org/10.1007/978-981-99-6544-1_7
Web address (URL) of conference proceedingshttps://link.springer.com/book/9789819965434
Web address (URL)https://www.icdam-conf.com/
Copyright holder© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Additional information

Use of archived accepted manuscripts (AMs) of non open-access books and chapters are subject to an embargo period and Springer Nature's AM terms of use, which permit users to view, print, copy, download and text and data-mine the content, for the purposes of academic research, subject always to the full conditions of use. Under no circumstances may the AM be shared or distributed under a Creative Commons, or other form of open access license, nor may it be reformatted or enhanced. Final published version available here: https://link.springer.com/chapter/10.1007/978-981-99-6544-1_7

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