Abstract
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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Kotsakis, E., Wolski, A. (2001). MAPS: A Method for Identifying and Predicting Aberrant Behavior in Time Series. In: Monostori, L., Váncza, J., Ali, M. (eds) Engineering of Intelligent Systems. IEA/AIE 2001. Lecture Notes in Computer Science(), vol 2070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45517-5_35
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DOI: https://doi.org/10.1007/3-540-45517-5_35
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