用于多变量时间序列模式探索的可解释AI：基于Temporal Fusion Transformer和变分自编码器的潜在空间可视化分析在电网事件诊断中的应用
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis
AI总结：
本文提出一种集成TFT和VAE的可视化分析框架，通过降维技术将复杂时间序列映射至2D潜在空间，直观探索电网事件特征，提升AI可解释性并支持故障诊断。
摘要：
检测和分析多变量时间序列数据中的复杂模式对于城市和环境系统运行中的决策至关重要。然而，复杂模式的高维性、错综复杂性以及相互关联的特性带来了挑战，阻碍了对其底层物理过程的理解。现有的AI方法在可解释性、计算效率和可扩展性方面常常面临局限，降低了其在实际场景中的适用性。本文提出了一种新颖的可视化分析框架，该框架集成了两种生成式AI模型，即Temporal Fusion Transformer (TFT)和Variational Autoencoders (VAEs)，将复杂模式降维至低维潜在空间，并使用PCA、t-SNE和UMAP与DBSCAN等降维技术将其在2D中可视化。这些可视化通过协调的交互视图和定制的字形呈现，能够直观地探索复杂的多变量时间模式，识别模式的相似性并揭示其潜在相关性，从而更好地解释AI输出。该框架通过电网信号数据的案例研究进行了演示，其中识别了多标签电网事件特征，包括具有不同根本原因的故障和异常。此外，还引入了新颖的指标和可视化方法，以验证模型并评估TFT和VAE在不同配置下生成的潜在地图的性能、效率和一致性。这些分析为模型参数调整和可靠性改进提供了可操作的见解。比较结果强调，与VAE相比，TFT实现了更短的运行时间以及对各种时间序列数据形状更优的可扩展性。这项工作推进了多变量时间序列中的故障诊断，促进了可解释AI以支持关键系统运行。
英文摘要：
Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns' similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and evaluate the performance, efficiency, and consistency of latent maps generated by TFT and VAE under different configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
