# Detecting Anomalous Event Sequences with Temporal Point Processes

arXiv: https://arxiv.org/abs/2106.04465 · HTML: https://mlsanity.com/paper/2106.04465/

## 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 (most cited first, 14 of 14)

- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Likelihood Ratios for Out-of-Distribution Detection](https://mlsanity.com/paper/1906.02845/) (2019)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation](https://mlsanity.com/paper/1602.03253/) (2016)
- [A Kernel Test of Goodness of Fit](https://mlsanity.com/paper/1602.02964/) (2016)
- [Intensity-Free Learning of Temporal Point Processes](https://mlsanity.com/paper/1909.12127/) (2019)
- [Neural Temporal Point Processes: A Review](https://mlsanity.com/paper/2104.03528/) (2021)
- [Density of States Estimation for Out-of-Distribution Detection](https://mlsanity.com/paper/2006.09273/) (2020)
- [Causal structure based root cause analysis of outliers](https://mlsanity.com/paper/1912.02724/) (2019)
- [Lecture Notes: Temporal Point Processes and the Conditional Intensity Function](https://mlsanity.com/paper/1806.00221/) (2018)
- [Learning Temporal Point Processes via Reinforcement Learning](https://mlsanity.com/paper/1811.05016/) (2018)
- [Neural Temporal Point Processes For Modelling Electronic Health Records](https://mlsanity.com/paper/2007.13794/) (2020)
- [Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality](https://mlsanity.com/paper/1906.02994/) (2019)
- [Further Analysis of Outlier Detection with Deep Generative Models](https://mlsanity.com/paper/2010.13064/) (2020)

## Built on by (most cited first, 6 of 6)

- [Prompt-augmented Temporal Point Process for Streaming Event Sequence](https://mlsanity.com/paper/2310.04993/) (2023)
- [LAST SToP For Modeling Asynchronous Time Series](https://mlsanity.com/paper/2502.01922/) (2025)
- [Unsupervised Event Outlier Detection in Continuous Time](https://mlsanity.com/paper/2411.16427/) (2024)
- [Latent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generation](https://mlsanity.com/paper/2606.24982/) (2026)
- [Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns](https://mlsanity.com/paper/2607.05855/) (2026)
- [Hand-Aware Transition Modeling for Bimanual Procedural Anomaly Detection](https://mlsanity.com/paper/2609.21207/) (2026)

## 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.