Multivariate Temporal Regression at Scale: A Three-Pillar Framework Combining ML, XAI, and NLP
This addresses the challenge of noisy, redundant, or biased datasets in dynamic domains like agriculture and energy, though it appears incremental as it combines existing techniques into a framework.
The paper tackles the problem of discovering actionable relationships in high-dimensional temporal data by integrating ML, XAI, and NLP to enhance data quality and streamline workflows, resulting in a 40-60% reduction in time to uncover insights and improved performance metrics like MSE, R2, and MAE.
This paper introduces a novel framework that accelerates the discovery of actionable relationships in high-dimensional temporal data by integrating machine learning (ML), explainable AI (XAI), and natural language processing (NLP) to enhance data quality and streamline workflows. Traditional methods often fail to recognize complex temporal relationships, leading to noisy, redundant, or biased datasets. Our approach combines ML-driven pruning to identify and mitigate low-quality samples, XAI-based interpretability to validate critical feature interactions, and NLP for future contextual validation, reducing the time required to uncover actionable insights by 40-60%. Evaluated on real-world agricultural and synthetic datasets, the framework significantly improves performance metrics (e.g., MSE, R2, MAE) and computational efficiency, with hardware-agnostic scalability across diverse platforms. While long-term real-world impacts (e.g., cost savings, sustainability gains) are pending, this methodology provides an immediate pathway to accelerate data-centric AI in dynamic domains like agriculture and energy, enabling faster iteration cycles for domain experts.