Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection
This addresses the challenge of handling diverse anomalies in time series data for applications like monitoring and forecasting, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of detecting anomalies in complex time series data by introducing TADNet, an end-to-end model that uses seasonal-trend decomposition to link anomalies to specific components, resulting in state-of-the-art performance validated on real-world datasets.
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.