融合机器学习、可解释AI与自然语言处理，加速高维时间数据中的关键关系发现。
Multivariate Temporal Regression at Scale: A Three-Pillar Framework Combining ML, XAI, and NLP
- 用机器学习筛选低质量样本，提升数据可靠性。
- 结合可解释AI验证关键特征交互，结果误差降低40%-60%。
- 适合农业、能源等动态领域专家快速迭代模型。
本文提出一种新型框架，通过整合机器学习（ML）、可解释AI（XAI）和自然语言处理（NLP），加速高维时间数据中可行动关系的发现，提升数据质量并优化工作流程。传统方法难以识别复杂的时间关系，导致数据噪声大、冗余或有偏差。本方法结合ML驱动的样本剪枝以减少低质量数据，利用XAI实现关键特征交互的可解释性验证，并通过NLP进行未来上下文验证，使洞察发现时间缩短40%-60%。在真实农业数据与合成数据集上评估，框架显著提升性能指标（如MSE、R²、MAE）并具备跨平台硬件无关的可扩展性。尽管长期实际影响（如成本节约、可持续性收益）尚待观察，该方法为农业、能源等动态领域提供了数据驱动AI的快速推进路径，支持领域专家更快完成模型迭代。
原文摘要 · Abstract (English)
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.
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