Detecting Anomalous Event Sequences with Temporal Point Processes

Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann

Keywords: temporal point processes, out-of-distribution detection, anomaly detection, event data, generative models

TL;DR: Approach for detecting anomalous continuous-time event sequences using goodness-of-fit statistics and neural TPP models.

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.

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Code: https://github.com/shchur/tpp-anomaly-detection

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2106.04465/code)