CHRONOGRAPH: A Real-World Graph-Based Multivariate Time Series Dataset

Luțu Adrian-Cătălin, Ioana Pintilie, Elena Burceanu, Andrei Manolache

Keywords: time series, anomaly detection, forecasting

TL;DR: We introduce CronoGraph, a graph-structured multivariate time series forecasting dataset built from real-world production microservices, annotated with expert anomaly labels.

Abstract:

We present CHRONOGRAPH, a graph-structured multivariate time series forecast-
ing 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 depen-
dencies 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 fore-
casting and incident-aware evaluation in microservice systems. Our dataset and
code are publicly available at https://github.com/bit-ml/ChronoGraph.

Submission Number: 16