# A comparative evaluation of novelty detection algorithms for discrete sequences

arXiv: https://arxiv.org/abs/1902.10940 · HTML: https://mlsanity.com/paper/1902.10940/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 5 of 5)

- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)
- [Novelty Detection in Sequential Data by Informed Clustering and Modeling](https://mlsanity.com/paper/2103.03943/) (2021)
- [Integrating Novelty Detection Capabilities with MSL Mastcam Operations to Enhance Data Analysis](https://mlsanity.com/paper/2103.12815/) (2021)
- [Role-based lateral movement detection with unsupervised learning](https://mlsanity.com/paper/2108.02713/) (2021)
- [Novelty-based Generalization Evaluation for Traffic Light Detection](https://mlsanity.com/paper/2201.00531/) (2022)

## Abstract (arXiv)

The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of the novelty detection problem applied to discrete sequences. The objective of this study is to identify which state-of-the-art methods are efficient and appropriate candidates for a given use case. These recommendations rely on extensive novelty detection experiments based on a variety of public datasets in addition to novel industrial datasets. We also perform thorough scalability and memory usage tests resulting in new supplementary insights of the methods' performance, key selection criterion to solve problems relying on large volumes of data and to meet the expectations of applications subject to strict response time constraints.