Cryptocurrency Price Prediction Using a Hybrid Deep Learning Approach: Integrating CNN and Random Forest for Enhanced Forecasting

Abstract

Cryptocurrency price prediction remains a challenging task due to high market volatility, complex price fluctuations, and external influencing factors. Traditional financial models struggle to capture these dynamic patterns, leading to inconsistent forecasting accuracy. This research presents a hybrid deep learning and machine learning approach that integrates convolutional neural networks (CNN) for feature extraction and random forest (RF) for interpretability. By utilizing historical price data from Binance, CoinGecko, and CoinMarketCap, the model leverages both numerical indicators and graphical price representations to improve prediction reliability. The results demonstrate that the hybrid model achieves higher accuracy compared to standalone models. The model records a mean absolute error (MAE) of 0.042 and a root mean square error (RMSE) of 0.078, outperforming both individual CNN and RF models. Feature importance analysis reveals that trading volume, moving averages, and MACD are the most influential factors in price forecasting. Additionally, the model successfully identifies profitable trading signals, achieving an overall prediction accuracy of 89.7 percent. These findings highlight the effectiveness of integrating deep learning with machine learning for cryptocurrency price prediction. The proposed model enhances prediction accuracy and decision-making, making it a valuable tool for traders and financial analysts navigating volatile cryptocurrency markets.