What are the most effective deep learning algorithms for classifying and predicting cryptocurrency trends?
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The most effective deep learning algorithms for classifying and predicting cryptocurrency trends are Long Short Term Memory (LSTM), Attention Long Short Term Memory (ALSTM), and Gate Recurrent Unit and Bidirectional–LSTM hybrid models. LSTM has been found to be optimal for predicting Bitcoin prices with an accuracy of 88.9% . ALSTM, when used with a combination of technical, trading, and social media indicators, significantly improves prediction accuracy for Ethereum and Bitcoin prices . Additionally, the Gate Recurrent Unit and Bidirectional–LSTM hybrid model have shown very good results in predicting various cryptocurrencies with RMSE values close to 0 and MAPE values below 10% . These deep learning models, particularly LSTM and its variants, have demonstrated superior performance in forecasting cryptocurrency values, making them valuable tools for investors and researchers in the cryptocurrency market.
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| Papers (5) | Insight |
|---|---|
| The most effective deep learning algorithm for predicting cryptocurrency trends is the Long Short-Term Memory (LSTM) algorithm, as demonstrated in the study. | |
01 Jul 2022 | The most effective deep learning algorithms for classifying and predicting cryptocurrency trends are LSTM, ALSTM, CNN, and MLP, showing improved accuracy when incorporating trading and social media indicators. |
| The hybrid model of Gated Recurrent Unit and Bidirectional-Long Short-Term Memory (Bi-LSTM) is effective for predicting cryptocurrency trends with improved accuracy. | |
27 Oct 2022 | LSTM is the most effective deep learning algorithm for predicting Bitcoin price and trend with 88.9% accuracy, as per the study. |
27 Oct 2022 | LSTM is the most effective deep learning algorithm for predicting Bitcoin price and trend with 88.9% accuracy, as per the study. |
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