Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation
This work addresses the problem of improving anomaly detection accuracy for time series data, but it is incremental as it evaluates existing methods without introducing new techniques.
The study compared using original time series data versus a new tabular representation from tsfresh for anomaly detection, finding that the new representation significantly improved Isolation Forest's performance across five datasets.
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.