Hybrid LSTM-GRU Model for Bitcoin PricePrediction

Abstract

Bitcoin price prediction is a popular topic in finance and technology circles. Developing an accurate bitcoin price prediction algorithm is crucial for the cryptocurrency market's growth and development. The development of bitcoin price prediction algorithms is challenging because bitcoin prices fluctuate heavily. Many researchers have attempted to predict the future price of Bitcoin using a variety of methods. This paper presents a web-based application for Bitcoin price prediction using a Hybrid LSTM-GRU model. The experimental results show the results of the Hybrid LSTM-GRU model compared to other models, such as LSTM and GRU. The models were evaluated using various metrics such as mean absolute error, root mean squared error, and mean squared error. The findings indicated that our model outperformed other deep learning models with RMSE, MSE, and MAE values of 0.136, 0.018, and 0.105, respectively. A web application was built using the Streamlit library.