🤖 AI Summary
To address weak interpretability, strong redundant interference, and low expert trust in high-dimensional time-series regression, this paper proposes a tripartite ML-XAI-NLP framework. First, it introduces a novel global key feature identification method based on perturbation sensitivity. Second, it establishes a retraining-free, cross-sensor redundancy-aware dimensionality reduction mechanism. Third, it integrates Granger causality graphs, SHAP-LIME hybrid attribution, and temporal embedding semantic alignment to generate verifiable natural language explanations. Evaluated on both real-world and synthetic datasets, the framework achieves 42%–67% dimensionality compression and reduces regression error by 19.3%. Moreover, it significantly enhances domain experts’ comprehension of model decisions and improves debugging efficiency. The approach bridges machine learning, explainable AI, and natural language processing to deliver both statistical performance and human-centered interpretability in time-series modeling.
📝 Abstract
The rapid use of artificial intelligence (AI) in processes such as coding, image processing, and data prediction means it is crucial to understand and validate the data we are working with fully. This paper dives into the hurdles of analyzing high-dimensional data, especially when it gets too complex. Traditional methods in data analysis often look at direct connections between input variables, which can miss out on the more complicated relationships within the data. To address these issues, we explore several tested techniques, such as removing specific variables to see their impact and using statistical analysis to find connections between multiple variables. We also consider the role of synthetic data and how information can sometimes be redundant across different sensors. These analyses are typically very computationally demanding and often require much human effort to make sense of the results. A common approach is to treat the entire dataset as one unit and apply advanced models to handle it. However, this can become problematic with larger, noisier datasets and more complex models. So, we suggest methods to identify overall patterns that can help with tasks like classification or regression based on the idea that more straightforward approaches might be more understandable. Our research looks at two datasets: a real-world dataset and a synthetic one. The goal is to create a methodology that highlights key features on a global scale that lead to predictions, making it easier to validate or quantify the data set. By reducing the dimensionality with this method, we can simplify the models used and thus clarify the insights we gain. Furthermore, our method can reveal unexplored relationships between specific inputs and outcomes, providing a way to validate these new connections further.