Enhancing Predictive Robustness in Financial Time Series Analysis through Cross-Modal Transformer Networks Leveraging Denoised Wavelet Representations and Latent Factor Modeling

Abstract

The inherent volatility and non-stationary nature of financial markets present a formidable challenge for traditional predictive modeling frameworks. As global financial infrastructures become increasingly interconnected, the demand for robust, high-fidelity forecasting systems has intensified. This research introduces a novel architectural paradigm centered on Cross Modal Transformer Networks that integrate denoised wavelet representations with latent factor modeling to enhance predictive stability. Unlike conventional approaches that treat financial data as a singular linear stream, the proposed system decomposes complex time series into multi-resolution components to isolate underlying structural signals from high-frequency market noise. By leveraging a cross-modal transformer architecture, the system facilitates the exchange of information between distinct temporal scales and latent economic drivers, enabling a more holistic interpretation of market dynamics. This study provides an extensive system-level analysis of the integration of signal processing and deep learning within financial infrastructures. We examine the structural trade-offs between computational latency and predictive accuracy, the governance challenges associated with automated financial decision-making, and the ethical implications of deploying highly complex algorithmic models in sensitive economic environments. Furthermore, the paper discusses the sustainability of such large-scale systems in terms of energy consumption and long-term maintenance. The findings suggest that the fusion of multi-resolution signal decomposition and multi-head attention mechanisms significantly improves the resilience of financial models against regime shifts and systemic shocks, providing a blueprint for more reliable and transparent financial forecasting infrastructures.