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Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection
Deep learning-based sequence models are extensively employed in Time Series Anomaly Detection (TSAD) tasks due to their effective sequential modeling capabilities.
MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction
Experiments on 32 real-world OD datasets proved the effectiveness of the proposed methods.
Simple Neighborhood Representative Pre-processing Boosts Outlier Detectors
Over the decades, traditional outlier detectors have ignored the group-level factor when calculating outlier scores for objects in data by evaluating only the object-level factor, failing to capture the collective outliers.
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