Transformer-Based Discovery of Dynamic Relationships in High-Frequency Financial Time Series

High-frequency financial data presents unique challenges for relationship discovery due to its complex temporal dependencies, microstructure effects, and rapidly evolving market dynamics. This paper presents a novel transformer-based framework specifically designed to discover and analyze dynamic relationships in high-frequency financial time series. Our approach leverages multi-head self-attention mechanisms of transformers to capture both short-term microstructure patterns and long-term dependencies while maintaining interpretability for financial practitioners. We introduce adaptive temporal position encoding that accounts for irregular trading intervals and market microstructure effects, specialized multi-head attention architectures designed to capture different types of market relationships at various scales, and temporal pattern recognition techniques that can identify time-varying correlations, lead-lag effects, and volatility clustering patterns. The framework incorporates advanced attention mechanisms with temporal pattern extraction capabilities and hyperparameter optimization strategies specifically designed for high-frequency data characteristics. Experimental evaluation demonstrates superior performance in discovering meaningful relationships compared to traditional econometric methods and standard deep learning approaches, with validation loss improvements of up to 40% across different model configurations. Our results reveal previously unknown intraday relationship patterns, provide insights into microstructure-driven correlations, and demonstrate the framework's ability to adapt to changing market regimes through attention weight analysis.