MAPS: A method for identifying and predicting aberrant behaviour in time series

Evangelos Kotsakis, Antoni Wolski

Research output: Book/ReportReport


We present a method for inducing a set of rules from time series data, which is originated monitoring 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, 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 mining in a seamless ways. Some of the existing mining algorithms have been modified to accommodate the need of discovering fuzzy event pattern.
Original languageEnglish
Place of PublicationEspoo
PublisherVTT Technical Research Centre of Finland
Number of pages13
Publication statusPublished - 2000
MoE publication typeD4 Published development or research report or study

Publication series

SeriesVTT Information Technology. Research Report


  • modelling behaviour
  • rule extraction
  • mining even structures Mining Aberrant Pattern in Sequences


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