A Survey on Cryptocurrency Price Prediction using Hybrid Approaches of Deep Learning Models
| dc.contributor.author | Bonthu Kotaiah N | |
| dc.date.accessioned | 2025-06-27T07:49:54Z | |
| dc.date.available | 2025-06-27T07:49:54Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Deep-learning and machine-learning algorithms have recently become a prominent research topic for forecasting the price of cryptocurrencies. Some research indicates that deep learning models are incapable of accurately and promptly predicting daily cryptocurrency prices, whereas other research compares the efficacy of various models. Such techniques include machine learning, deep learning, and statistical models, among others. Several studies have devised hybrid approaches that combine novel methodologies in an effort to enhance the accuracy of bitcoin price forecasts. Complex models of deep learning and interdependent relationships are examples of these modern methods. To further improve the quality of survey data, there are additional datasets that making frequent errors. The search results indicate that efforts are being made to better bitcoin price estimations using deep learning and hybrid methods. © 2023 IEEE. | |
| dc.identifier.uri | http://ctuap.ndl.gov.in/handle/123456789/79 | |
| dc.language.iso | en | |
| dc.title | A Survey on Cryptocurrency Price Prediction using Hybrid Approaches of Deep Learning Models | |
| dc.type | Conference Paper |