Comparative Analysis on Deep Learning Models for Cryptocurrency Prediction

Cryptocurrency price prediction carries important social and economic implications as it directly influences financial markets and personal investments. Statistically, it is essential but difficult to make accurate predictions because of high volatility and sophisticated market behavior. Precision and responsiveness are difficult to achieve, and this has been demonstrated by conventional predictive models like ARIMA and basic recurrent neural networks. It is difficult for traditional machine learning models to identify sophisticated market patterns. This paper helps to overcome these challenges by investigating advanced deep learning methods, namely Temporal Fusion Transformer (TFT), Graph Neural Networks and ARIMA Hybrid Models, integrated with specialized data transformation methods. These modern approaches help in improving the price prediction accuracy, which is higher than that of conventional models, with Graph Neural Networks (GNN) demonstrating accuracy over 90% for all leading cryptocurrencies. Initial results show that in the process of forecasting price direction for cryptocurrencies such as Bitcoin, Ethereum, Litecoin, and Binance Coin, GNN has consistently outperformed traditional methods while TFT has also performed well as a runner up model.