Enhancing Cryptocurrency Price Prediction and Analysis through Deep Learning

Abstract

Predicting Bitcoin prices remains a complex challenge due to the cryptocurrency market's inherent volatility and rapid fluctuations. Based on historical price data gathered from Investing.com over the previous five years, this study investigates the potency of three sophisticated deep learning models for predicting Bitcoin prices: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM). Rigid backtesting and comparative analysis were used to assess each model's ability to predict future Bitcoin values. With an accuracy of 98.02%, a Mean Absolute Error (MAE) of 1023.48, a Root Mean Square Error (RMSE) of 1576.11, and an R2 score of 0.9904, the findings show that the GRU model performs better than the LSTM and Bi-LSTM models. An accuracy of 97.99% was attained by the LSTM model with an MAE of 961.70, an RMSE of 1426.48, and an R2 score of 0.9921; on the other hand, a 96.91% accuracy was attained by the Bi-LSTM model with an MAE of 1441.28, an RMSE of 1848.11, and an R2 score of 0.9867. This study shows that the GRU model performs better than the other models and demonstrates the usefulness of deep learning methods for predicting Bitcoin prices. Additionally included in the paper are the effects of these models on market volatility, ethical issues in cryptocurrency trading, and financial decision-making. The results provide the groundwork for future lines of inquiry that may include outside influences and create real-time prediction systems. In summary, this study advances financial technology by showing that complex neural network architectures can accurately predict Bitcoin prices and provide information about potential future research directions for enhancing predictive models and tackling ethical and environmental issues in the cryptocurrency space.