真实微服务系统多变量时间序列数据集，含依赖图与故障标签。
ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset
- 基于真实微服务构建带依赖关系的多变量时序数据
- 提供专家标注故障窗口，支持异常检测评估
- 适合研究结构感知预测与故障响应的系统开发者
我们提出ChronoGraph，一个基于真实生产环境微服务的图结构多变量时间序列预测数据集。每个节点代表一个生成系统级性能指标（如CPU、内存、网络使用率）流的服务，有向边表示服务间的依赖关系。主要任务是在服务级别预测未来信号值。此外，数据集包含专家标注的故障时间段作为异常标签，可用于评估异常检测方法及分析预测在运维中断下的鲁棒性。相比工业控制系统或交通、空气质量等领域的现有基准，ChronoGraph独特地融合了（i）多变量时间序列，（ii）显式可机器读取的依赖图，（iii）与真实事件对齐的异常标签。我们报告了涵盖预测模型、预训练时间序列基础模型和标准异常检测器的基线结果。ChronoGraph为研究微服务系统中的结构感知预测与事件感知评估提供了现实基准。
原文摘要 · Abstract (English)
We present ChronoGraph, a graph-structured multivariate time series forecasting dataset built from real-world production microservices. Each node is a service that emits a multivariate stream of system-level performance metrics, capturing CPU, memory, and network usage patterns, while directed edges encode dependencies between services. The primary task is forecasting future values of these signals at the service level. In addition, ChronoGraph provides expert-annotated incident windows as anomaly labels, enabling evaluation of anomaly detection methods and assessment of forecast robustness during operational disruptions. Compared to existing benchmarks from industrial control systems or traffic and air-quality domains, ChronoGraph uniquely combines (i) multivariate time series, (ii) an explicit, machine-readable dependency graph, and (iii) anomaly labels aligned with real incidents. We report baseline results spanning forecasting models, pretrained time-series foundation models, and standard anomaly detectors. ChronoGraph offers a realistic benchmark for studying structure-aware forecasting and incident-aware evaluation in microservice systems.
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