  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.
