# Multivariate Temporal Regression at Scale: A Three-Pillar Framework Combining ML, XAI, and NLP

arXiv: https://arxiv.org/abs/2504.02151 · HTML: https://mlsanity.com/paper/2504.02151/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics](https://mlsanity.com/paper/2009.10795/) (2020)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Not All Samples Are Created Equal: Deep Learning with Importance Sampling](https://mlsanity.com/paper/1803.00942/) (2018)

## Built on by (most cited first, 1 of 1)

- [Interpretable AI for Time-Series: Multi-Model Heatmap Fusion with Global Attention and NLP-Generated Explanations](https://mlsanity.com/paper/2507.00234/) (2025)

## Abstract (arXiv)

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.