ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection

• URL: http://arxiv.org/abs/2606.01300v1
• Date: Sun, 31 May 2026 15:42:40 GMT
• Title: ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
• Abstract summary: Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry.<n>We introduce ChronosAD, a novel architecture for anomaly detection that uses a time series foundation model as a feature extractor.<n>Our model requires minimal task-specific tuning and demonstrates robust generalization across a wide range of domains, including industrial, medical, cyber-physical, and automotive systems.
• Score: 18.00932623960289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across different datasets, especially when anomalies are subtle or context-dependent. To solve this issue, we introduce ChronosAD, a novel architecture for anomaly detection that uses a time series foundation model as a feature extractor. Specifically, it employs a two-stage pipeline: first, it uses the foundation model to extract embeddings for each time series in a zero-shot manner. Then, a custom-developed Temporal Block, composed of Bidirectional Long Short-Term Memory (BiLSTM) and Multi-Head Attention, refines these embeddings to capture temporal dependencies and highlight salient patterns. Unlike previous approaches, our model requires minimal task-specific tuning and demonstrates robust generalization across a wide range of domains, including industrial, medical, cyber-physical, and automotive systems. Extensive experiments on 11 benchmarks show that ChronosAD outperforms existing methods by 4.72% in AUC and 6.60% in AP on average. The source code is available at https://github.com/intelligolabs/ChronosAD.

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