CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation
Anomalous time series play a critical role in safety-critical domains, yet they are inherently scarce, heterogeneous, and costly to obtain. Existing time series generation methods predominantly focus on synthesizing normal data, providing limited value when anomalous samples are needed. We identify two fundamental challenges in anomaly generation: (i) the scarcity of anomaly data, and (ii) the heterogeneous morphological characteristics of anomalies. To address these challenges, we propose CAST, a Context- and Anomaly Structure-conditioned Time series anomaly generation framework with principled two-stage pretraining and finetuning strategy. In pretraining stage, we leverage abundant norm...
| Authors | Haochen Zhang, Jie Peng, Songyuan Sui et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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