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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mohammad, Y. Nishida, T. |
| Copyright Year | 2011 |
| Description | Author affiliation: Graduate School of Informatics, Kyoto University, Japan (Nishida, T.) || Electrical Engineering Department, Assiut University, Egypt (Mohammad, Y.) |
| Abstract | Complex systems involve the interaction between many processes that may or may not have causal relations to each other. In such systems, discovering causal relations can provide significant insights into the internals of the system and facilitate fault discovery and recovery procedures. In this paper, we provide a novel causality detection algorithm based on robust singular spectrum transform that combines features of autoregressive modeling and perturbation analysis. The proposed approach was evaluated using both synthetic and real data and was shown to provide superior performance to the standard linear Granger-causality test. It also provides a natural way to detect common causes that may give false positives in other causality tests. |
| Starting Page | 12 |
| Ending Page | 17 |
| File Size | 490746 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457715235 |
| e-ISBN | 9781457715242 |
| DOI | 10.1109/SII.2011.6147411 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-12-20 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Change detection algorithms Time series analysis Detectors Vectors Silicon Delay Robots |
| Content Type | Text |
| Resource Type | Article |
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