| Autor: |
S., Sujana, Jairam, Bhat Geetalaxmi |
| Předmět: |
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| Zdroj: |
Grenze International Journal of Engineering & Technology (GIJET); Jan2024, Vol. 10 Issue 1, Part 2, p1228-1235, 8p |
| Abstrakt: |
Bitcoin has recently attracted a lot of media and public attention as a result of its recent price boom and crash. Similarly, numerous researchers have explored many aspects influencing the Bitcoin price and the patterns underlying its fluctuations, specifically utilizing various machine learning methods. In this work, authors investigate and analyze various cutting-edge machine learning algorithms for Bitcoin price prediction, such as a logistic regression and a long short-term memory (LSTM) model. Although LSTM-based prediction models outperformed other prediction models for Bitcoin price prediction (regression), a simple profitability analysis revealed that classification models were more effective than regression models for algorithmic trading. Overall, the suggested ML learning-based prediction models performed similarly. This work performs an in-depth investigation on the evolution of Bitcoin, as well as a thorough review of several machine learning methods used for price prediction. It’s included is a Bitcoin price prediction. The Existing work fails to predict day to day bitcoin price changes. And has given less accuracy compared to the proposed work. Authors have overcome this limitation in the proposed work by predicting the day to day BTC price for the upcoming month. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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