EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments

Raza, H, Girijesh, P and Li, Y 2015, 'EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments' , Pattern Recognition, 48 (3) , pp. 659-669.

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Dataset shift is a very common issue wherein the input data distribution shifts over time in non-stationary environments. A broad range of real-world systems face the challenge of dataset shift. In such systems, continuous monitoring of the process behavior and tracking the state of shift are required in order to decide about initiating adaptive corrections in a timely manner. This paper presents novel methods for covariate shift-detection tests based on a two-stage structure for both univariate and multivariate time-series. The first stage works in an online mode and it uses an exponentially weighted moving average (EWMA) model based control chart to detect the covariate shift-point in non-stationary time-series. The second stage validates the shift-detected by first stage using the Kolmogorov–Smirnov statistical hypothesis test (K–S test) in the case of univariate time-series and the Hotelling T-Squared multivariate statistical hypothesis test in the case of multivariate time-series. Additionally, several orthogonal transformations and blind source separation algorithms are investigated to counteract the adverse effect of cross-correlation in multivariate time-series on shift-detection performance. The proposed methods are suitable to be run in real-time. Their performance is evaluated through experiments using several synthetic and real-world datasets. Results show that all the covariate shifts are detected with much reduced false-alarms compared to other methods.

Item Type: Article
Themes: Media, Digital Technology and the Creative Economy
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Pattern Recognition
Publisher: Elsevier
Refereed: Yes
ISSN: 0031-3203
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 01 Dec 2014 16:43
Last Modified: 15 Feb 2022 15:47
URI: http://usir.salford.ac.uk/id/eprint/33195

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