Bitcoin Price Prediction Based on CNN-Bi-LSTM-Attention Model
DOI:
https://doi.org/10.54097/hbem.v16i.10540Keywords:
Convolutional Neural Network (CNN), Bidirectional LSTM, Attention mechanism, Bitcoin, Prediction Model.Abstract
Due to many factors, Bitcoin has experienced huge price fluctuations since its emergence, and it has received extensive attention. Forecasting the price of bitcoin is of great significance for investors and for the country's future development. This paper collects the data of bitcoin price and indicator that may affect the price, and then use random forest algorithm for feature selection to remove all nonessential indicators. Then, CNN-Bi-LSTM-Attention model is built to train the data and predict the price of bitcoin. Finally, this model is compared with other models. It can be found that this model has higher prediction accuracy and better prediction effect than traditional models such as LSTM and CNN-LSTM.
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