Transformer-Based Deep Learning for Financial Time-Series Forecasting: A Multi-Horizon Prediction Framework
Abstract
The rapid evolution of deep learning architectures has fundamentally altered the landscape of financial econometrics and predictive modeling. Traditional linear and autoregressive models, while foundational to financial theory, often fail to capture the high-frequency volatility, non-linear dependencies, and long-range temporal correlations inherent in modern globalized markets. This paper explores the transition toward attention-based mechanisms, specifically focusing on Transformer architectures as a robust framework for multi-horizon financial time-series forecasting. Unlike recurrent structures that suffer from vanishing gradients and sequential processing bottlenecks, the self-attention mechanism enables the simultaneous processing of vast historical datasets, facilitating the identification of structural breaks and regime shifts across multiple temporal scales. This research provides a comprehensive systems-level analysis of the integration of Transformer models within socio-technical financial infrastructures. We examine the architectural trade-offs between computational complexity and predictive accuracy, the role of positional encoding in preserving temporal order, and the systemic implications of deploying such models in high-stakes trading environments. Furthermore, the paper addresses critical dimensions of algorithmic governance, including the interpretability of attention weights, the ethical considerations of market-wide model convergence, and the environmental sustainability of large-scale deep learning deployments. By synthesizing insights from computer science, financial engineering, and public policy, we propose a multi-horizon framework that balances predictive power with systemic stability, offering a roadmap for the next generation of resilient financial AI systems.
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