TimeRCD, a transformer pre-trained on 2.5 billion synthetic data points with context-relative anomaly labels, beats reconstruction-based foundation models on most zero-shot TSAD benchmarks, while the evaluation tunes its context window per test dataset.
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Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
TimeRCD, a transformer pre-trained on 2.5 billion synthetic data points with context-relative anomaly labels, beats reconstruction-based foundation models on most zero-shot TSAD benchmarks, while the evaluation tunes its context window per test dataset.