Deep Temporal Convolutional Networks for High Frequency Cryptocurrency Price Forecasting

Abstract

This survey reviews the application of Deep Temporal Convolutional Networks (TCNs) in high-frequency cryptocurrency price forecasting, a field challenged by extreme volatility and non-stationary dynamics. Recent studies demonstrate that TCNs achieve superior performance over traditional machine learning models and recurrent architectures by efficiently capturing long-range temporal dependencies through parallelizable structures. We synthesize findings across different market regimes, highlighting TCNs' advantages in computational efficiency, robustness, and adaptability to rapidly shifting trading environments. Moreover, the review examines emerging efforts to enhance interpretability, addressing a key barrier to real-world adoption in financial systems. By consolidating current progress and open challenges, this paper underscores the significance of TCNs as a promising direction for building more reliable forecasting frameworks, while outlining future opportunities to strengthen their practical impact in algorithmic trading and financial decision-making.