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 realworld datasets. Results show effectiveness of the proposed approach in terms of decreased type-I error and tolerable increase in detection time delay. © IFIP International Federation for Information Processing 2013.
CITATION STYLE
Raza, H., Prasad, G., & Li, Y. (2013). EWMA based two-stage dataset shift-detection in non-stationary environments. In IFIP Advances in Information and Communication Technology (Vol. 412, pp. 625–635). Springer New York LLC. https://doi.org/10.1007/978-3-642-41142-7_63
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