AI-Driven Cryptocurrency Price Prediction: Evaluating Transfer Learning

14 January 2026, by Omkar Kondhalkar

Photo: base.camp

Extreme volatility and hidden market dependencies make cryptocurrency price forecasting a persistent challenge. To address this, the work investigates deep learning models for high-frequency price prediction, comparing Multilayer Perceptrons, LSTM networks, and Time-Series Transformers. A central focus is transfer learning, where models are pre-trained on Bitcoin and fine-tuned on correlated altcoins to capture broader market structures. This approach challenges the common assumption that assets should be modeled in isolation. Model performance is evaluated using statistical error measures alongside simulated trading strategies, enabling an assessment of both predictive accuracy and economic relevance. The results aim to improve the robustness of short-term forecasting in highly dynamic financial markets.

Main aim to build a model that is not only statistically accurate but also robust against the stochastic nature of crypto markets, as discussed in"Fusion in Cryptocurrency Price Prediction: A Decade Survey" (Patel et al.) .