Raza, Haider, Prasad, Girijesh and Li, Yuhua ![]() |
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 Weighted Moving 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.
Item Type: | Conference or Workshop Item (Paper) |
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Date Type: | Publication |
Status: | Published |
Schools: | Computer Science & Informatics |
Publisher: | Springer |
ISBN: | 978-3-642-41141-0 |
ISSN: | 1868-4238 |
Last Modified: | 07 Nov 2022 09:26 |
URI: | https://orca.cardiff.ac.uk/id/eprint/129152 |
Citation Data
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