DL-Crypto: Deep Learning Techniques for Cryptocurrency Price Prediction

Cryptocurrency markets are known for their extreme volatility, presenting substantial opportunities and risks for traders and investors. The predictive accuracy of cryptocurrency prices is crucial for strategic investment and financial planning. Normal finance models often miss out the complexity and sudden movements that can be found in cryptocurrency data and this leads to poor prediction performance. Traditional models like ARIMA and Exponential Smoothing are commonly used to forecast time series but face difficulties with the nonlinear patterns and high volatility which are characteristic of cryptocurrency markets. This paper proposes an integrated approach using advanced deep learning architectures, including TFT-LSTM, GNNLSTM, CVAE-Transformer, NODE-GRU, and CA-ConvLSTM, designed to handle the complexities of cryptocurrency data effectively. The study uses a comprehensive dataset sourced from Yahoo Finance, encompassing features such as Open, High, Low, Close, and Volume (OHLCV), along with technical indicators like moving averages and volatility indexes, which are essential for the predictive models. The ensemble model combining the strengths of individual architectures outperformed each standalone model, demonstrating superior accuracy with reduced prediction error across multiple evaluation metrics. The proposed ensemble deep learning framework significantly enhances the accuracy of cryptocurrency price predictions. Future work could integrate realtime data streams and market sentiment analysis to further refine the predictions and adapt to market changes dynamically.