Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation
Machine Learning
2025-01-22 v2
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.
Cite
@article{arxiv.2501.07999,
title = {Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation},
author = {Marine Hamon and Vincent Lemaire and Nour Eddine Yassine Nair-Benrekia and Samuel Berlemont and Julien Cumin},
journal= {arXiv preprint arXiv:2501.07999},
year = {2025}
}
Comments
7