mlsanityData to 7 Oct 2026

TODS: An Automated Time Series Outlier Detection System

arXiv 2009.09822 ↗First posted 18 Sep 2020cs.DB cs.LG stat.MLcited by 41

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Abstract (arXiv)

We present TODS, an automated Time Series Outlier Detection System for research and industrial applications. TODS is a highly modular system that supports easy pipeline construction. The basic building block of TODS is primitive, which is an implementation of a function with hyperparameters. TODS currently supports 70 primitives, including data processing, time series processing, feature analysis, detection algorithms, and a reinforcement module. Users can freely construct a pipeline using these primitives and perform end- to-end outlier detection with the constructed pipeline. TODS provides a Graphical User Interface (GUI), where users can flexibly design a pipeline with drag-and-drop. Moreover, a data-driven searcher is provided to automatically discover the most suitable pipelines given a dataset. TODS is released under Apache 2.0 license at https://github.com/datamllab/tods.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2020.

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Citing papers per year: 2021: 3; 2022: 16; 2023: 12; 2024: 3; 2025: 4; 2026: 3.

41 papers.

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  1. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  2. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  3. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  4. 2022Deep Isolation Forest for Anomaly Detectioncited by 90
  5. 2022BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphscited by 80
  6. 2023FinGPT: Democratizing Internet-scale Data for Financial Large Language Modelscited by 65
  7. 2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 58
  8. 2023Data-centric AI: Perspectives and Challengescited by 43
  9. 2022PyGOD: A Python Library for Graph Outlier Detectioncited by 31
  10. 2022AutoShard: Automated Embedding Table Sharding for Recommender Systemscited by 17
  11. 2022Sintel: A Machine Learning Framework to Extract Insights from Signalscited by 16
  12. 2022ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labelscited by 15
29 more
  1. 2022DreamShard: Generalizable Embedding Table Placement for Recommender Systemscited by 14
  2. 2022Towards Similarity-Aware Time-Series Classificationcited by 13
  3. 2023ADGym: Design Choices for Deep Anomaly Detectioncited by 11
  4. 2022Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learningcited by 9
  5. 2022TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attackcited by 9
  6. 2021Learning Disentangled Representations for Time Seriescited by 8
  7. 2023Context-aware Domain Adaptation for Time Series Anomaly Detectioncited by 8
  8. 2021AutoVideo: An Automated Video Action Recognition Systemcited by 6
  9. 2023Fast Unsupervised Deep Outlier Model Selection with Hypernetworkscited by 6
  10. 2022MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detectioncited by 5
  11. 2026AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoningcited by 3
  12. 2023From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Managementcited by 2
  13. 2024Critical Review for One-class Classification: recent advances and the reality behind themcited by 2
  14. 2021Outlier Detection using AI: A Surveycited by 1
  15. 2022Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasetscited by 1
  16. 2023Towards Personalized Preprocessing Pipeline Searchcited by 1
  17. 2023An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zonecited by 1
  18. 2024Outlier Ranking in Large-Scale Public Health Streamscited by 1
  19. 2026FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Modelscited by 1
  20. 2022A Review of Open Source Software Tools for Time Series Analysiscited by 0
  21. 2022Generative Anomaly Detection for Time Series Datasetscited by 0
  22. 2023Interactive System-wise Anomaly Detectioncited by 0
  23. 2023Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Servicescited by 0
  24. 2023Time Series Synthesis Using the Matrix Profile for Anonymizationcited by 0
  25. 2024An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Frameworkcited by 0
  26. 2025Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detectioncited by 0
  27. 2025Machine Unlearning for Robust DNNs: Attribution-Guided Partitioning and Neuron Pruning in Noisy Environmentscited by 0
  28. 2025Reasoning-based Anomaly Detection Framework: A Real-time, Scalable, and Automated Approach to Anomaly Detection Across Domainscited by 0
  29. 2026Unified Taxonomy for Multivariate Time Series Anomaly Detection using Deep Learningcited by 0