Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2310.00268v2
• Date: Thu, 14 Dec 2023 08:24:45 GMT
• Title: Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection
• Authors: Zhenwei Zhang, Ruiqi Wang, Ran Ding, Yuantao Gu
• Abstract summary: Traditional Time-series Anomaly Detection (TAD) methods often struggle with the composite nature of complex time-series data. We introduce TADNet, an end-to-end TAD model that leverages Seasonal-Trend Decomposition to link various types of anomalies to specific decomposition components. Our training methodology, which includes pre-training on a synthetic dataset followed by fine-tuning, strikes a balance between effective decomposition and precise anomaly detection.
• Score: 22.002053911451604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional Time-series Anomaly Detection (TAD) methods often struggle with the composite nature of complex time-series data and a diverse array of anomalies. We introduce TADNet, an end-to-end TAD model that leverages Seasonal-Trend Decomposition to link various types of anomalies to specific decomposition components, thereby simplifying the analysis of complex time-series and enhancing detection performance. Our training methodology, which includes pre-training on a synthetic dataset followed by fine-tuning, strikes a balance between effective decomposition and precise anomaly detection. Experimental validation on real-world datasets confirms TADNet's state-of-the-art performance across a diverse range of anomalies.

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