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arXiv · 2501.07999

Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation

Marine Hamon·Vincent Lemaire·Nour Eddine Yassine Nair-Benrekia·Samuel Berlemont·Julien Cumin
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Abstract

To detect anomalies with precision and without prior knowledge in time series, is it better to build a detector from the initial temporal representation, or to compute a new (tabular) representation using an existing automatic variable construction library? In this article, we address this question by conducting an in-depth experimental study for two popular detectors (Isolation Forest and Local Outlier Factor). The obtained results, for 5 different datasets, show that the new representation, computed using the tsfresh library, allows Isolation Forest to significantly improve its performance.

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Marine Hamon, Vincent Lemaire, Nour Eddine Yassine Nair-Benrekia, Samuel Berlemont, Julien Cumin. 2025-01-14. Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation. https://arxiv.org/abs/2501.07999

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