Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 34 references that are in the map. First posted on arXiv 14 Jan 2025.
Builds on
12 of 34 references.
References · 12
- 2022ADBench: Anomaly Detection Benchmarkcited by 232
- 2020A review on outlier/anomaly detection in time series datacited by 189
- 2019Time-Series Anomaly Detection Service at Microsoftcited by 165
- 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 151
- 2019catch22: CAnonical Time-series CHaracteristicscited by 105
- 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 97
- 2020Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Artcited by 56
- 2020TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networkscited by 55
- 2024Dive into Time-Series Anomaly Detection: A Decade Reviewcited by 24
- 2022The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifiercited by 18
- 2022Open challenges for Machine Learning based Early Decision-Making researchcited by 6
- 2023Automatic Feature Engineering for Time Series Classification: Evaluation and Discussioncited by 3
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Citing papers per year: 2025: 1.
1 paper.