{"ID":2921776,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-03T05:56:00.181519634Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01300","arxiv_id":"2606.01300","title":"ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection","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.","short_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...","url_abs":"https://arxiv.org/abs/2606.01300","url_pdf":"https://arxiv.org/pdf/2606.01300v1","authors":"[\"Uzair Khan\",\"Luigi Capogrosso\",\"Francesco Biondani\",\"Michele Magno\",\"Franco Fummi\",\"Francesco Setti\",\"Marco Cristani\"]","published":"2026-05-31T15:42:40Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":612601,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-02T02:42:49.606572591Z","DeletedAt":null,"paper_id":2921776,"paper_url":"https://arxiv.org/abs/2606.01300","paper_title":"ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection","repo_url":"https://github.com/intelligolabs/ChronosAD","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}