mlsanityData to 14 Oct 2026

MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection

arXiv 2205.11755 ↗First posted 24 May 2022cs.LGcited by 6

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Abstract (arXiv)

Organizations leverage anomaly and changepoint detection algorithms to detect changes in user behavior or service availability and performance. Many off-the-shelf detection algorithms, though effective, cannot readily be used in large organizations where thousands of users monitor millions of use cases and metrics with varied time series characteristics and anomaly patterns. The selection of algorithm and parameters needs to be precise for each use case: manual tuning does not scale, and automated tuning requires ground truth, which is rarely available. In this paper, we explore MOSPAT, an end-to-end automated machine learning based approach for model and parameter selection, combined with a generative model to produce labeled data. Our scalable end-to-end system allows individual users in large organizations to tailor time-series monitoring to their specific use case and data characteristics, without expert knowledge of anomaly detection algorithms or laborious manual labeling. Our extensive experiments on real and synthetic data demonstrate that this method consistently outperforms using any single algorithm.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 13 of its 43 references that are in the map. First posted on arXiv 24 May 2022.

Builds on

13 of 43 references.

References · 13

  1. 2019Time-Series Anomaly Detection Service at Microsoftcited by 165
  2. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 88
  3. 2020An Evaluation of Change Point Detection Algorithmscited by 63
  4. 2020Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Artcited by 56
  5. 2020TODS: An Automated Time Series Outlier Detection Systemcited by 41
  6. 2021Merlion: A Machine Learning Library for Time Seriescited by 25
  7. 2018Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergencescited by 21
  8. 2020PyODDS: An End-to-end Outlier Detection System with Automated Machine Learningcited by 21
  9. 2016Changepoint Detection in the Presence of Outlierscited by 9
  10. 2021cleanTS: Automated (AutoML) Tool to Clean Univariate Time Series at Microscalescited by 7
  11. 2017On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Datacited by 5
  12. 2020Building an Automated and Self-Aware Anomaly Detection Systemcited by 3
1 more
  1. 2021Self-supervised learning for fast and scalable time series hyper-parameter tuningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 0; 2025: 1; 2026: 1.

6 papers.

  1. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 7
  2. 2023Adaptive Thresholding Heuristic for KPI Anomaly Detectioncited by 3
  3. 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
  4. 2023Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detectioncited by 1
  5. 2023Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Servicescited by 0
  6. 2026Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detectioncited by 0