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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chen-ju Lin Yen-ting Chen |
| Copyright Year | 2012 |
| Description | Author affiliation: Dept. of Ind. Eng. & Manage., Yuan Ze Univ., Chungli, Taiwan (Chen-ju Lin; Yen-ting Chen) |
| Abstract | Timely detection of an event is one of the important tasks in many spatiotemporal applications. The unknown nature of the event makes the detection process challenging. Likelihood ratio (LR) methods have been widely used for identifying locations and change point times through searching the maximum evidence over temporal and spatial windows. However, the LR-based methods may not be optimal if shift parameters are unknown. This paper introduces a new test framework for spatiotemporal surveillance based on the EWMA technique. We propose a statistic that applies exponential smoothing to both temporal and spatial axes and aggregate spatiotemporal data in each scan window. The results show that the EWMA-based method could be more or less sensitive than the LR-based methods depending on the chosen smoothing parameter, size of scan window, underlying shift patterns and time of occurrence. |
| Sponsorship | IEEE Singapore Sect. |
| Starting Page | 382 |
| Ending Page | 385 |
| File Size | 1540410 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467329453 |
| ISSN | 2157362X |
| DOI | 10.1109/IEEM.2012.6837766 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-12-10 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Smoothing methods Surveillance EWMA Control charts Cluster Spatiotemporal phenomena Steady-state Diseases scan statistic |
| Content Type | Text |
| Resource Type | Article |
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