Sequential Outlier Detection in Non-Stationary Time Series
Statistics Theory
2025-02-26 v1 Methodology
Statistics Theory
Abstract
A novel method for sequential outlier detection in non-stationary time series is proposed. The method tests the null hypothesis of ``no outlier'' at each time point, addressing the multiple testing problem by bounding the error probability of successive tests, using extreme value theory. The asymptotic properties of the test statistic are studied under the null hypothesis and alternative. The finite sample properties of the new detection scheme are investigated by means of a simulation study, and the method is compared with alternative procedures which have recently been proposed in the statistics and machine learning literature.
Cite
@article{arxiv.2502.18038,
title = {Sequential Outlier Detection in Non-Stationary Time Series},
author = {Florian Heinrichs and Patrick Bastian and Holger Dette},
journal= {arXiv preprint arXiv:2502.18038},
year = {2025}
}
Comments
24 pages, 3 figures