mlsanityData to 14 Oct 2026

Detecting Anomalous Event Sequences with Temporal Point Processes

arXiv 2106.04465 ↗First posted 8 Jun 2021cs.LGcited by 6

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Abstract (arXiv)

Automatically detecting anomalies in event data can provide substantial value in domains such as healthcare, DevOps, and information security. In this paper, we frame the problem of detecting anomalous continuous-time event sequences as out-of-distribution (OoD) detection for temporal point processes (TPPs). First, we show how this problem can be approached using goodness-of-fit (GoF) tests. We then demonstrate the limitations of popular GoF statistics for TPPs and propose a new test that addresses these shortcomings. The proposed method can be combined with various TPP models, such as neural TPPs, and is easy to implement. In our experiments, we show that the proposed statistic excels at both traditional GoF testing, as well as at detecting anomalies in simulated and real-world data.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 14 of its 63 references that are in the map. First posted on arXiv 8 Jun 2021.

Builds on

14 of 63 references.

References · 14

  1. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  2. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 453
  3. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  4. 2016A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluationcited by 236
  5. 2016A Kernel Test of Goodness of Fitcited by 143
  6. 2019Intensity-Free Learning of Temporal Point Processescited by 140
  7. 2021Neural Temporal Point Processes: A Reviewcited by 114
  8. 2020Density of States Estimation for Out-of-Distribution Detectioncited by 74
  9. 2019Causal structure based root cause analysis of outlierscited by 65
  10. 2018Lecture Notes: Temporal Point Processes and the Conditional Intensity Functioncited by 40
  11. 2018Learning Temporal Point Processes via Reinforcement Learningcited by 40
  12. 2020Neural Temporal Point Processes For Modelling Electronic Health Recordscited by 39
2 more
  1. 2019Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicalitycited by 31
  2. 2020Further Analysis of Outlier Detection with Deep Generative Modelscited by 31

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 3.

6 papers.

  1. 2023Prompt-augmented Temporal Point Process for Streaming Event Sequencecited by 24
  2. 2025LAST SToP For Modeling Asynchronous Time Seriescited by 5
  3. 2024Unsupervised Event Outlier Detection in Continuous Timecited by 0
  4. 2026Latent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generationcited by 0
  5. 2026Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patternscited by 0
  6. 2026Hand-Aware Transition Modeling for Bimanual Procedural Anomaly Detectioncited by 0