Detecting Anomalous Event Sequences with Temporal Point Processes

2021/06/08 by Oleksandr Shchur, Ali Caner Türkmen, Shchur, Oleksandr +7
Computer Science · Decision Sciences · Medicine · #Advanced Statistical Process Monitoring #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Healthcare Technology and Patient Monitoring #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2106.04465

openalex publication_date 2021/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes
• Rescaling, thinning or complementing? On goodness-of-fit procedures for\n point process models and Generalized Linear Models
• Lecture Notes: Temporal Point Processes and the Conditional Intensity Function
• Learning Temporal Point Processes via Reinforcement Learning
• WAIC, but Why? Generative Ensembles for Robust Anomaly Detection
• Causal structure based root cause analysis of outliers

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