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Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Time 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.
- A new visual analytics framework is proposed to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D.
- The framework integrates Time Fusion Transformer (TFT) and Variational Autoencoders (VAEs) to enable intuitive exploration of complex multivariate temporal patterns.
- A case study on power grid signal data demonstrates the framework's ability to identify multi-label grid event signatures and evaluate model performance and efficiency.
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