Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy

Time series anomaly detection(TSAD) is a critical task, but developing models that generalize to unseen data in a zero-shot manner remains challenging. Existing foundation models for TSAD often rely on reconstruction-error scoring at inference time, which can miss subtle anomalies that are well reconstructed and can falsely flag complex but normal patterns in unseen domains. We introduce TimeRCD, a foundation model for TSAD built on Relative Context Discrepancy (RCD), a pre-training paradigm that trains the model to detect anomalies by comparing a query pattern with its surrounding context. This relational formulation, implemented with a standard Transformer architecture, enables the model to infer normality from the input context rather than relying on fixed global normal patterns. We further construct a large-scale synthetic corpus with context-dependent anomaly labels to provide supervised pre-training signals for RCD. Experiments across diverse benchmarks show that TimeRCD outperforms existing general-purpose and anomaly-specific foundation models in most zero-shot TSAD settings, while remaining competitive with dataset-specific full-shot baselines. These results provide empirical evidence that RCD is an effective direction for building robust and generalizable TSAD models.

Many real-world systems, such as hospitals, factories, and cloud services, need to spot unusual time patterns early, but current AI methods often miss subtle problems or raise false alarms when normal behavior looks unfamiliar. We propose TimeRCD, which trains a Transformer to judge each pattern by comparing it with its surrounding context, using a large synthetic collection of diverse, labeled time-series anomalies instead of relying on reconstruction errors. This helps anomaly detectors work more reliably on new domains without extra training data, making it easier to catch rare failures before they cause costly or dangerous consequences.