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Bitcoin price and return prediction based on LSTM
- 1 Soochow University
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Abstract
This paper focuses on the prediction of Bitcoin prices and returns based on the Long Short Term Memory (LSTM) neural network model, to better consider the impact of time factors. Since Bitcoin has long dominated the digital currency trading market, many researchers have completed many Bitcoin prediction results, including the screening of optimal features, comparison of prediction models and classification of prediction problems. Based on previous work, this article adds a Bitcoin revenue forecast section, presenting the results in the form of charts and data to provide more intuitive trends and more accurate performance. This paper uses LSTM as the experimental model, and uses the Bitcoin transaction history data set with timestamps as the original input. After a specific normalization method, the original model is trained, and then the subsequent transaction data is predicted. Compare it with the real value in the data set to get the final experimental results show that in this prediction problem, the performance of LSTM is slightly better than Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost); on the other hand, compared with price prediction based on real values for prediction, the prediction fluctuations of return are more obvious and more realistic, providing better reference value.
Keywords
Bitcoin price, deep learning, prediction, feature
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Cite this article
Yang,R. (2023). Bitcoin price and return prediction based on LSTM. Theoretical and Natural Science,26,74-80.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
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