# A comparative evaluation of novelty detection algorithms for discrete sequences

## TL;DR

This paper compares various state-of-the-art novelty detection algorithms for discrete sequences, evaluating their efficiency, scalability, and suitability for different applications using diverse datasets.

## Contribution

It provides a comprehensive experimental comparison and practical recommendations for selecting effective novelty detection methods for discrete sequences.

## Key findings

- Certain algorithms outperform others in scalability and accuracy
- Memory usage varies significantly across methods
- Recommendations depend on data volume and response time requirements

## Abstract

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.