Enhancing Price Prediction for BTC, ETH, LTC, and XRP through Hybrid Deep Learning Models and External Factors

Abstract

"Considering the cryptocurrency market's volatility, price prediction for cryptocurrency is very challenging. There are various factors like news, and social media news that affect the prices of cryptocurrency. To address this volatility, issue the author has included sentiments in the price prediction. The author has used a hybrid deep learning model by combining two deep learning algorithms namely LSTM and GRU. For the evaluation of the model, metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) were calculated. Future improvements include real-time sentiments to the model to make a better accuracy."