# Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series

arXiv: https://arxiv.org/abs/2010.05073 · HTML: https://mlsanity.com/paper/2010.05073/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2020.

## Built on by (most cited first, 30 of 56)

- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [TAB: Unified Benchmarking of Time Series Anomaly Detection Methods](https://mlsanity.com/paper/2506.18046/) (2025)
- [TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis](https://mlsanity.com/paper/2210.09693/) (2022)
- [Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection](https://mlsanity.com/paper/2207.12201/) (2022)
- [ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2307.00754/) (2023)
- [Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders](https://mlsanity.com/paper/2405.15273/) (2024)
- [Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection](https://mlsanity.com/paper/2405.15370/) (2024)
- [TSGBench: Time Series Generation Benchmark](https://mlsanity.com/paper/2309.03755/) (2023)
- [Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series](https://mlsanity.com/paper/2303.01272/) (2023)
- [TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models](https://mlsanity.com/paper/2402.10802/) (2024)
- [See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers](https://mlsanity.com/paper/2411.02465/) (2024)
- [Sintel: A Machine Learning Framework to Extract Insights from Signals](https://mlsanity.com/paper/2204.09108/) (2022)
- [A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis](https://mlsanity.com/paper/2209.04635/) (2022)
- [SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios](https://mlsanity.com/paper/2605.07161/) (2026)
- [Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback](https://mlsanity.com/paper/2507.15066/) (2025)
- [Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset](https://mlsanity.com/paper/2411.09047/) (2024)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series](https://mlsanity.com/paper/2506.23596/) (2025)
- [xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection](https://mlsanity.com/paper/2506.22837/) (2025)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [Trustworthy Anomaly Detection: A Survey](https://mlsanity.com/paper/2202.07787/) (2022)
- [A Meta-level Analysis of Online Anomaly Detectors](https://mlsanity.com/paper/2209.05899/) (2022)
- [MLLM4TS: Leveraging Vision and Multimodal Language Models for General Time-Series Analysis](https://mlsanity.com/paper/2510.07513/) (2025)
- [BALANCE: Bayesian Linear Attribution for Root Cause Localization](https://mlsanity.com/paper/2301.13572/) (2023)
- [Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon](https://mlsanity.com/paper/2408.04377/) (2024)
- [Matrix Profile for Anomaly Detection on Multidimensional Time Series](https://mlsanity.com/paper/2409.09298/) (2024)
- [SoftED: Metrics for Soft Evaluation of Time Series Event Detection](https://mlsanity.com/paper/2304.00439/) (2023)
- [MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection](https://mlsanity.com/paper/2510.26643/) (2025)

## Abstract (arXiv)

Access to high-quality data repositories and benchmarks have been instrumental in advancing the state of the art in many experimental research domains. While advanced analytics tasks over time series data have been gaining lots of attention, lack of such community resources severely limits scientific progress. In this paper, we present Exathlon, the first comprehensive public benchmark for explainable anomaly detection over high-dimensional time series data. Exathlon has been systematically constructed based on real data traces from repeated executions of large-scale stream processing jobs on an Apache Spark cluster. Some of these executions were intentionally disturbed by introducing instances of six different types of anomalous events (e.g., misbehaving inputs, resource contention, process failures). For each of the anomaly instances, ground truth labels for the root cause interval as well as those for the extended effect interval are provided, supporting the development and evaluation of a wide range of anomaly detection (AD) and explanation discovery (ED) tasks. We demonstrate the practical utility of Exathlon's dataset, evaluation methodology, and end-to-end data science pipeline design through an experimental study with three state-of-the-art AD and ED techniques.