Dataset shift detection in non-stationary environments using EWMA charts

Raza, Haider, Prasad, Girijesh and Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478 2014. Dataset shift detection in non-stationary environments using EWMA charts. Presented at: 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK, 13-16 Oct 2013. 2013 IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, New Jersey: IEEE, pp. 3151-3156. 10.1109/SMC.2013.537

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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)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	978-0-7695-5154-8
Last Modified:	07 Nov 2022 09:26
URI:	https://orca.cardiff.ac.uk/id/eprint/129151

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