Labels Matter More Than Models: Quantifying the Benefit of Supervised Time Series Anomaly Detection

Abstract--Time series anomaly detection (TSAD) is a critical data mining task often constrained by label scarcity. Consequently, current research predominantly focuses on Unsupervised Time-series Anomaly Detection (UT AD), relying on complex architectures to model normal data distributions. However, this approach often overlooks the significant performance gains available from limited anomaly labels achievable in practical scenarios. This paper challenges the premise that architectural complexity is the optimal path for TSAD. We conduct the first methodical comparison between supervised and unsupervised paradigms and introduce STAND, a streamlined supervised baseline. Extensive experiments on five public datasets demonstrate that: (1) Labels matter more than models: under a limited labeling budget, simple supervised models significantly outperform complex state-of-the-art unsupervised methods; (2) Supervision yields higher returns: the performance gain from minimal supervision far exceeds that from architectural innovations; and (3) Practicality: STAND exhibits superior prediction consistency and anomaly localization compared to unsupervised counterparts. These findings advocate for a data-centric shift in TSAD research, emphasizing label utilization over purely algorithmic complexity. The code is publicly available at https://github.com/EmorZz1G/ST IME series anomaly detection (TSAD) is a crucial and challenging task in time series data mining, with broad applications in fields such as industrial system monitoring, cybersecurity, and health surveillance [1, 2, 3, 4]. Due to the scarcity of anomaly samples and the high cost of labeling in TSAD, unsupervised time series anomaly detection (UT AD) methods have garnered significant attention in recent years [5, 3, 6, 7]. Typically, unsupervised methods assume that the training time series data primarily consists of normal samples.