Deep Learning in Financial Time Series Analysis: A Comprehensive Review of Methods, Challenges, and Future Directions

Abstract

This comprehensive review examines the application of deep learning techniques in financial time series analysis, with a particular focus on asset price prediction and market volatility forecasting. Through a systematic analysis of over 80 studies and technical sources published between 2014 and 2025—including peer-reviewed journal articles, conference proceedings, preprints, and industry reports—we identify key architectural advances, methodological innovations, and emerging research directions. Our findings reveal that transformer-based models and hybrid architectures consistently outperform traditional econometric approaches, achieving up to 25% improvement in prediction accuracy. However, significant challenges remain in model interpretability, generalization across market regimes, and the integration of multimodal data sources. We identify critical research gaps in explainable AI (XAI) for financial decision-making, transfer learning for emerging markets, and online adaptive learning systems. This review provides a structured framework for understanding the current state of the field and highlights key opportunities for advancing deep learning-based financial forecasting.