Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
arXiv:2509.21190v4 Announce Type: replace Abstract: Time series anomaly detection (TSAD) is a critical task, but developing models that generalize to unseen data in a zero-shot manner remains a major challenge. Prevailing foundation models for TSAD predominantly rely on reconstruction-based objectives, which suffer from a fundamental objective mismatch: they struggle to identify subtle anomalies while often misinterpreting complex normal patterns, leading to high rates of false negatives and positives. To overcome these limitations, we introduce \texttt{TimeRCD}, a novel foundation model for T
The proliferation of time series data across industries and the inherent limitations of current anomaly detection models necessitate new foundational approaches.
Zero-shot time series anomaly detection is critical for real-time monitoring, predictive maintenance, and cybersecurity across diverse domains without extensive retraining.
The introduction of a foundation model like TimeRCD shifts the paradigm from reconstruction-based TSAD to context-discrepancy, improving accuracy and reducing false positives/negatives.
- · AI/ML researchers
- · Cloud infrastructure providers
- · Industrial IoT sectors
- · Cybersecurity firms
- · Legacy anomaly detection software vendors
- · Firms reliant on traditional manually-tuned models
Improved reliability and efficiency in automated systems that depend on accurate anomaly detection.
Reduced operational costs and increased uptime across critical infrastructure and industrial processes.
The development of more resilient and autonomous systems, potentially accelerating progress in complex AI agentic architectures.
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Read at arXiv cs.LG