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Dataset Shift Detection in Non-stationary Environments Using EWMA Charts

Raza, Haider, Prasad, G and Li, Yuhua (2013) Dataset Shift Detection in Non-stationary Environments Using EWMA Charts. In: IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK.. IEEE. 6 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6722290

DOI: 10.1109/SMC.2013.537

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 contribution (Paper)
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:28716
Deposited By: Professor Girijesh Prasad
Deposited On:25 Feb 2014 12:16
Last Modified:25 Feb 2014 12:16

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