用预训练模型做时间序列异常检测，自动调整灵敏度且结果可解释。
Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring
- 基于预训练模型预测，自适应加权分位数构建置信区间。
- 在分布偏移下仍保持稳定误报率，支持快速部署。
- 无需微调，适合数据少、资源受限的工业场景。
我们提出一种后处理自适应共形异常检测方法，用于监控时间序列，利用预训练基础模型的预测结果，无需额外微调。该方法生成的异常得分可直接解释为误报率（p值），有助于透明且可操作的决策。通过加权分位数共形预测边界，并从历史预测中自适应学习最优权重参数，实现分布漂移下的校准与稳定的误报控制，同时保留样本外保证。作为模型无关方案，它可无缝集成于基础模型，支持在资源受限环境中快速部署。该方法解决了工业中数据稀缺、缺乏训练经验及即时推理需求等关键挑战，充分利用时间序列基础模型日益普及的优势。在合成与真实数据集上的实验表明，该方法性能优异，兼具简单性、可解释性、鲁棒性与自适应能力。
原文摘要 · Abstract (English)
We propose a post-hoc adaptive conformal anomaly detection method for monitoring time series that leverages predictions from pre-trained foundation models without requiring additional fine-tuning. Our method yields an interpretable anomaly score directly interpretable as a false alarm rate (p-value), facilitating transparent and actionable decision-making. It employs weighted quantile conformal prediction bounds and adaptively learns optimal weighting parameters from past predictions, enabling calibration under distribution shifts and stable false alarm control, while preserving out-of-sample guarantees. As a model-agnostic solution, it integrates seamlessly with foundation models and supports rapid deployment in resource-constrained environments. This approach addresses key industrial challenges such as limited data availability, lack of training expertise, and the need for immediate inference, while taking advantage of the growing accessibility of time series foundation models. Experiments on both synthetic and real-world datasets show that the proposed approach delivers strong performance, combining simplicity, interpretability, robustness, and adaptivity.
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