التفاصيل البيبلوغرافية
| العنوان: |
A Comparative Study On Crypto Currency Prediction Using Modern Deep Learning Techniques. |
| المؤلفون: |
Ghorpade, Naveen, Baraki, Parashuram, Biradar, Arun, M. R., Mahesh Kumar |
| المصدر: |
Turkish Online Journal of Qualitative Inquiry; 2021, Vol. 12 Issue 10, p2282-2291, 10p |
| مصطلحات موضوعية: |
CRYPTOCURRENCY exchanges, MATHEMATICAL optimization, SOCIAL conflict, PREDICTION models, COMPARATIVE studies, CRYPTOCURRENCIES, DEEP learning |
| مستخلص: |
Cryptocurrencies are now well-established and widely accepted as a form of alternative trading money. Because they have invaded nearly all accounting operations, cryptocurrency trading is often regarded as popular and propitious sorts of successful investments. A reliable forecasting and prediction model is essential for predicting the high volatility caused by the increase in financial market. This current work summarises various optimisation techniques by shedding light on the volatility factors like internal competition, market pressure, financial problems, social and political conflicts, etc. A comprehensive study is presented on how to predict the volatility of cryptocurrency and formulate their inter-connections with the parent Cryptocurrencies using various deep learning frameworks. Further, this work focuses on utilising sophisticated models to improve the forecasting performance of cryptocurrency. We are hopeful that this work will provide broader insights in the future research of cryptocurrency. [ABSTRACT FROM AUTHOR] |
|
Copyright of Turkish Online Journal of Qualitative Inquiry is the property of Turkish Online Journal of Qualitative Inquiry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder