Raza, H, Prasad, G and Li, Y 2013, Dataset shift detection in non-stationary environments using EWMA charts , in: Institute of Electrical and Electronics Engineers (IEEE) International Conference on Systems, Man, and Cybernetics, 13-16 October 2013, Manchester.
Full text not available from this repository.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) |
|---|---|
| Schools: | Schools > School of Computing, Science and Engineering |
| Journal or Publication Title: | Systems, Man, and Cybernetics (SMC), 2013 Institute of Electrical and Electronics Engineers (IEEE) International Conference |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Refereed: | Yes |
| Related URLs: | |
| Funders: | Non funded research |
| Depositing User: | Yuhua Li |
| Date Deposited: | 27 Jul 2015 10:58 |
| Last Modified: | 06 Sep 2021 07:39 |
| URI: | http://usir.salford.ac.uk/id/eprint/33110 |
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