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

DOI:
10.60864/3hsc-x603
Citation Author(s):
Zhenwei Zhang, Ruiqi Wang, Ran Ding, Yuantao Gu
Submitted by:
Zhenwei Zhang
Last updated:
6 June 2024 - 10:27am
Document Type:
Presentation Slides
Document Year:
2024
Event:
Presenters:
Zhenwei Zhang
Paper Code:
https://github.com/zhangzw16/TADNet
 

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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