Conditional attribution retrieves contextually similar normal states from VAE latent spaces and UMAP embeddings to explain time-series anomalies while preserving dependencies, improving root-cause accuracy on SWaT and MSDS benchmarks.
arXiv preprint arXiv:2010.05073 (2020)
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Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection
Conditional attribution retrieves contextually similar normal states from VAE latent spaces and UMAP embeddings to explain time-series anomalies while preserving dependencies, improving root-cause accuracy on SWaT and MSDS benchmarks.