MAPS: A Method for Identifying and Predicting Aberrant Behavior in Time Series
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We present a method for inducing a set of rules from time series data, which is originated from a monitored process. The proposed method is called MAPS (Mining Aberrant Patterns in Sequences) and it may be used in decision support or in control to identify faulty system states. It consists of four parts: training, identification, event mining and prediction. In order to improve the flexibility of the event identification, we employ fuzzy sets and propose a method that extracts membership functions from statistical measures of the time series. The proposed approach integrates fuzzy logic and event mining in a seamless way. Some of the existing event mining algorithms have been modi- fied to accommodate the need of discovering fuzzy event patterns.
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- MAPS: A Method for Identifying and Predicting Aberrant Behavior in Time Series
- Book Title
- Engineering of Intelligent Systems
- Book Subtitle
- 14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2001 Budapest, Hungary, June 4–7, 2001 Proceedings
- pp 314-325
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- Lecture Notes in Computer Science
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- Springer Berlin Heidelberg
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- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 1. Hungarian Academy of Sciences, Intelligent Manufacturing and Business Processes Computer and Automation Research Institute
- 2. Department of Computer Science 601 University Drive, Southwest Texas State University
- Author Affiliations
- 5. Joint Research Center (CCR), Space Application Institute, Via Enrico Fermi 1, TP 261, I-21020, Ispra (VA), Italy
- 6. SOLID Applied Research Center, Merimiehenkatu 36 D, FIN-00150, Helsinki, Finland
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