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EWMA Model based Shift-Detection Methods for Detecting Covariate Shifts in Non-Stationary Environments

Raza, Haider, Prasad, G and Li, Yuhua (2015) EWMA Model based Shift-Detection Methods for Detecting Covariate Shifts in Non-Stationary Environments. Pattern Recognition, 48 (3). pp. 659-669. [Journal article]

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URL: http://www.sciencedirect.com/science/article/pii/S0031320314002878

DOI: 10.1016/j.patcog.2014.07.028


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 case of univariate time-series and Hotelling's T-Squared multivariate statistical hypothesis test in case of multivariate time-series. Additionally, several orthogonal transformation 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:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:30138
Deposited By: Professor Girijesh Prasad
Deposited On:12 Sep 2014 10:14
Last Modified:05 Jan 2015 11:51

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