Prediction of Cryptocurrency Price Using Deep Learning

Abstract

Cryptocurrencies have gained popularity since the launch of virtual currencies like Bitcoin, Ethereum, and many more. Because of their extreme volatility, the cryptocurrency markets present both opportunities and challenges for traders and investors. In this volatile market, accurate cryptocurrency price prediction is essential for making wise investment choices. In this research, created a ground-breaking deep learning-based technique for forecasting bitcoin values. LSTM, GRU, Random Forest Regressor (RFR), and Super Vector Regressor (SVR) are a few of the deep learning algorithms that are used. The proposed model considers a number of elements that may have an impact on the value of cryptocurrencies, including historical price information, trading volume, market mood, and technical signals. In this research cross model is suggested that combines LSTM and GRU for the accurate prediction. Based on the results of evaluation measures such RMSE, MAE, MSE, R-Square Error, and Variance Regression Score, the optimal model is selected. In comparison to previous deep learning models, the hybrid model of LSTM+GRU has performed well in price prediction, according to the results of evaluation metrics.