Summary of Explainable Ai For Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis, by Haowen Xu et al.
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis
by Haowen Xu, Ali Boyaci, Jianming Lian, Aaron Wilson
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed visual analytics framework integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to analyze complex patterns in multivariate time-series data. The framework reduces high-dimensional patterns into lower-dimensional latent spaces using dimensionality reduction techniques like PCA, t-SNE, and UMAP with DBSCAN. This enables intuitive exploration of complex patterns, identifying similarities and uncovering correlations for better AI output interpretability. The framework is demonstrated through a power grid signal data case study, where it identifies multi-label grid event signatures with diverse root causes. Novel metrics and visualizations are introduced to validate models and evaluate performance, efficiency, and consistency under different configurations. Comparative results show TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to understand complex patterns in urban and environmental systems using AI. It combines two types of AI models to make the patterns easier to see and understand. This helps with decision-making by showing how different patterns are related. The method is tested on power grid data and shows that it can identify different types of events, like faults and anomalies. The paper also introduces new ways to measure the performance of the AI models and evaluate their reliability. |
Keywords
* Artificial intelligence * Dimensionality reduction * Pca * Time series * Transformer * Umap
