CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation
CAST is proposed, a Context- and Anomaly Structure-conditioned Time series anomaly generation framework with principled two-stage pretraining and finetuning strategy that consistently outperforms state-of-the-art anomaly generation methods in terms of both generation fidelity and downstream task utility.
H. Zhang, Jie Peng, Song-Yuan Sui et al.
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