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.
Original language | English |
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Title of host publication | Artificial Intelligence Applications and Innovations |
Editors | Harris Papadopoulos, Andreas. S. Andreou, Lazaros IIiadis, IIias Maglogiannis |
Place of Publication | London |
Publisher | Springer |
Pages | 625-635 |
Volume | 412 |
ISBN (Print) | 978-3-642-41141-0 |
DOIs | |
Publication status | Published (in print/issue) - 1 Oct 2013 |