Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

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 underst...

תיאור מלא

מידע ביבליוגרפי

Principais autores:	Haowen Xu, Ali Boyaci, Jianming Lian, Aaron J. Wilson
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	IEEE 2026-01-01
סדרה:	IEEE Access
נושאים:	Explainable AI visual analytics variational autoencoders multivariate time-series Human-in-the-Loop generative AI
גישה מקוונת:	https://ieeexplore.ieee.org/document/11141392/
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!