CCE：面向时间序列异常检测的置信度-一致性评估
CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection
AI总结：
针对现有时间序列异常检测指标的多重局限，提出同时衡量预测置信度与不确定性一致性的CCE指标，兼具严格有界性、鲁棒性与线性复杂度，并构建首个标准化可复现的RankEval排序能力基准，二者均已开源。
摘要：
时间序列异常检测指标是模型评估的关键工具，但现有指标存在若干局限：判别能力不足、超参数依赖性强、对扰动敏感，且计算开销高。本文提出置信度-一致性评估（Confidence-Consistency Evaluation, CCE），这是一种同时衡量预测置信度与不确定性一致性的新型评估指标。我们通过贝叶斯估计量化异常分数的不确定性，为模型预测构建全局和事件级的置信度与一致性分数，最终得到简洁的CCE指标。理论与实验均证明，CCE具有严格有界性、对分数扰动的Lipschitz鲁棒性，以及线性时间复杂度$\boldsymbol{\text{O}(n)}$。此外，我们构建了RankEval基准，用于比较各类指标的排序能力。RankEval是首个标准化、可复现的评估流程，可实现评估指标的客观比较。CCE与RankEval的实现均已完全开源。
英文摘要：
Time Series Anomaly Detection metrics serve as crucial tools for model evaluation. However, existing metrics suffer from several limitations: insufficient discriminative power, strong hyperparameter dependency, sensitivity to perturbations, and high computational overhead. This paper introduces Confidence-Consistency Evaluation (CCE), a novel evaluation metric that simultaneously measures prediction confidence and uncertainty consistency. By employing Bayesian estimation to quantify the uncertainty of anomaly scores, we construct both global and event-level confidence and consistency scores for model predictions, resulting in a concise CCE metric. Theoretically and experimentally, we demonstrate that CCE possesses strict boundedness, Lipschitz robustness against score perturbations, and linear time complexity $\mathcal{O}(n)$. Furthermore, we establish RankEval, a benchmark for comparing the ranking capabilities of various metrics. RankEval represents the first standardized and reproducible evaluation pipeline that enables objective comparison of evaluation metrics. Both CCE and RankEval implementations are fully open-source.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
