EWMA based two-stage dataset shift-detection in non-stationary environments

Raza, H; Prasad, G; Li, Y

Abstract

Dataset shift is a major challenge in the non-stationary environments wherein the input data distribution may change over time. In a time-series data, detecting the dataset shift point, where the distribution 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 a novel method to detect the shift-point based on a two-stage structure involving Exponentially WeightedMoving Average (EWMA) chart and Kolmogorov-Smirnov test, which substantially reduces type-I error rate. The algorithm is suitable to be run in real-time. Its performance is evaluated through experiments using synthetic and real-world datasets. Results show effectiveness of the proposed approach in terms of decreased type-I error and tolerable increase in detection time delay.

• Publication Date: Jan 1, 2013
• Deposit Date: Jul 8, 2015
• Pages: 625-635
• Series Title: IFIP Advances in Information and Communication Technology
• Series Number: 412
• Book Title: Artificial Intelligence Applications and Innovations : 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30–October 2, 2013, Proceedings
• ISBN: 9783642411427
• DOI: https://doi.org/10.1007/978-3-642-41142-7_63
• Publisher URL: http://dx.doi.org/10.1007/978-3-642-41142-7_63
• Related Public URLs: http://link.springer.com/book/10.1007/978-3-642-41142-7