mTSBench：大规模多变量时间序列异常检测与模型选择基准测试
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
AI总结：
mTSBench是一个大规模多变量时间序列异常检测与模型选择基准测试，评估24种检测器并揭示模型选择的迫切需求。
摘要：
多变量时间序列中的异常检测在医疗、网络安全和工业监控等领域至关重要，但仍然面临根本性挑战，原因包括高维依赖性、时间依赖变量之间的交叉相关性以及标注异常数据稀缺。我们介绍了mTSBench，这是迄今为止最大的多变量时间序列异常检测和模型选择基准测试，包含来自广泛应用领域的19个数据集中的344个标注时间序列。我们全面评估了24种异常检测器，包括唯一两种公开可用的基于大语言模型的方法。与先前发现一致，我们发现没有单一检测器在所有数据集中占据主导地位，这促使了有效模型选择的必要性。我们基准测试了三种最近的模型选择方法，并发现即使最强的也远未达到最优。我们的结果突显了对稳健、可推广的选择策略的迫切需求。我们开源该基准测试，网址为https://plan-lab.github.io/mtsbench，以鼓励未来研究。
英文摘要：
Anomaly detection in multivariate time series is essential across domains such as healthcare, cybersecurity, and industrial monitoring, yet remains fundamentally challenging due to high-dimensional dependencies, the presence of cross-correlations between time-dependent variables, and the scarcity of labeled anomalies. We introduce mTSBench, the largest benchmark to date for multivariate time series anomaly detection and model selection, consisting of 344 labeled time series across 19 datasets from a wide range of application domains. We comprehensively evaluate 24 anomaly detectors, including the only two publicly available large language model-based methods for multivariate time series. Consistent with prior findings, we observe that no single detector dominates across datasets, motivating the need for effective model selection. We benchmark three recent model selection methods and find that even the strongest of them remain far from optimal. Our results highlight the outstanding need for robust, generalizable selection strategies. We open-source the benchmark at https://plan-lab.github.io/mtsbench to encourage future research.
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。
