A comparative evaluation of novelty detection algorithms for discrete sequences

Rémi Domingues*, Pietro Michiardi, Jérémie Barlet, Maurizio Filippone

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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 candidate methods for 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 criteria to solve problems relying on large volumes of data and to meet the expectations of applications subject to strict response time constraints.

Original languageEnglish (US)
Pages (from-to)3787-3812
Number of pages26
JournalArtificial Intelligence Review
Issue number5
StatePublished - Jun 1 2020


  • Anomaly detection
  • Discrete sequences
  • Fraud detection
  • Novelty detection
  • Outlier detection
  • Temporal data

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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