Comparative Analysis of Deep Learning Models for Cryptocurrency Price Predictions: Evidence based on Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and Solana (SOL).

Abstract

This research examines deep-learning and machine-learning model for cryptocurrency price prediction, keenly focusing Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Solana (SOL). Cryptocurrencies have a high volatility, a non-linear behaviour and are able to react strongly to exogenous events making their prediction and forecasting challenging. The primary aim of this research is to find out which predictive models provide optimal performance in characterizing these complexities and to offer empirical guidance on real-life investment and risk-management applications. Four approaches were used for this forecasting: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), a combination of LSTM-GRU models, and Stochastic Gradient Descent (SGD) regression. The daily historical data were used to train and test each model on different forecast horizons and performance was measured accordingly by Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. As shown in the results, it can be illustrated that GRU exhibited the lowest error rates in the majority of the assets, especially those in the short-term prediction. LSTM demonstrated a promising ability to capture long dependencies whereas the hybrid LSTM-GRU system showed a similar performance proficiency by combining the relative superiorities of the two respective models. On the other hand, the conventional SGD regression was the worst in terms of all the deep-learning algorithms hence proving the extreme capability of the algorithms in modelling non- linear time sequences. The results confirm GRU as the most viable model of AI-powered crypto prediction, and demonstrate the potential of hybrid architectures at least in a certain situation. This study will add to the existing debates about whether or not deep learning has a place in predicting financial outcomes and will provide usable information to traders, analysts, and researchers attempting to work in the uncertainty of the digital asset world.