- Presenting a comprehensive study of the various existing schemes to predict the prices of BTC, ETH, and LTC cryptocurrencies.
- Using AI algorithms such as LSTM, bi-LSTM, and GRU to accurately predict the prices of cryptocurrencies.
- Utilizing long short-term memory (LSTM), a deep learning algorithm, and Fbprophet, which is an auto machine learning algorithm, for prediction.
- Evaluating the proposed hybrid models using evaluation matrices such as RMSE and MAPE for Bitcoin, Ethereum, and Litecoin.
2. Literature Review
3. Materials and Methods
3.1. Machine Learning Algorithms
3.1.1. Long Short-Term Memory (LSTM)
3.1.2. Bidirectional LSTM (bi-LSTM)
3.1.3. Gated Recurrent Unit (GRU)
3.2. Evaluation Matrix
3.3. Data Exploration
5.1. Results for BTC
5.2. Results for ETH
5.3. Results for LTC
- The AI algorithm is reliable and acceptable for cryptocurrency prediction.
- GRU can predict cryptocurrency prices better than LSTM and bi-LSTM but overall all algorithms represent excellent predictive results.
Data Availability Statement
Conflicts of Interest
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|Variable Name||Variable Description||Data Type|
|Date||Date of Observation||Date|
|Open||Opening price on the given day||Number|
|High||High price on the given day||Number|
|Low||Low price on the given day||Number|
|Close||Close price on the given day||Number|
|||BTC, LTC||Multi-linear regression model||R2 score: 44% for LTC and 59% for BTC|
|||BTC||Logistic regression and linear discriminant analysis||LR: 66%|
|||BTC||ARIMA, LSTM and GRU.||RMSE ARIMA: 302.53, LSTM: 603.68|
|This paper||BTC||LSTM, GRU, and bi-LSTM||GRU|
|This paper||ETH||LSTM, GRU, and bi-LSTM||GRU|
|This paper||LTC||LSTM, GRU, and bi-LSTM||GRU|
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