PAI: Preserving Amplitude Information in Representation-Based Time-Series Anomaly Detection
Researchers have developed a new anomaly detection scoring scheme called PAI, designed to address the limitation of amplitude-agnostic embeddings in existing representation-based methods. PAI incorporates a diagnostic module to assess amplitude information capture and a score augmentation function that fuses representation scores with median deviation and local mean-shift scores. This approach significantly improves performance on datasets like TSB-AD-U-Eva and TAB UV, with one combination outperforming the state-of-the-art by 15%. The findings highlight the importance of retaining amplitude information in time-series anomaly detection. AI
IMPACT Enhances anomaly detection accuracy by explicitly incorporating amplitude information, potentially improving performance in critical applications.