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Dataset shift detection in non-stationary environments using EWMA charts

Raza, H, Prasad, G and Li, Y 2013, Dataset shift detection in non-stationary environments using EWMA charts , in: Institute of Electrical and Electronics Engineers (IEEE) International Conference on Systems, Man, and Cybernetics, 13-16 October 2013, Manchester.

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Abstract

Dataset shift is a major challenge in the non-stationary environments wherein the input data distribution may change over time. Detecting the dataset shift point in the time-series data, where the distribution of time-series changes its properties, is of utmost interest. Dataset shift exists in a broad range of real-world systems. In such systems, there is a need for continuous monitoring of the process behavior and tracking the state of the shift so as to decide about initiating adaptive corrections in a timely manner. This paper presents an algorithm to detect the shift-point in a non-stationary time-series data. The proposed method detects the shift-point based on an exponentially weighted moving average (EWMA) control chart for auto-correlated observations. This algorithm is suitable to be run in real-time and monitors the data to detect the dataset shift. Its performance is evaluated through experiments using synthetic and real-world datasets. Results show that all the dataset-shifts are detected without the delay.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Systems, Man, and Cybernetics (SMC), 2013 Institute of Electrical and Electronics Engineers (IEEE) International Conference
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 27 Jul 2015 10:58
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33110

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