MAPS: A Method for Identifying and Predicting Aberrant Behavior in Time Series
- First Online:
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.
Unable to display preview. Download preview PDF.
- 1.Rakesh Agrawal, Ramakrishnan Srikant: Mining Sequential Patterns. In Proceedings of the Eleventh IEEE International Conference on Data Engineering (ICDE’95), pp. 3–14, March 6-10, 1995, Taipei, Taiwan.Google Scholar
- 4.Tom Fawcett, Foster J. Provost: Activity Monitoring: Noticing Interesting Changes in Behavior. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’99), pp. 53–62, August 15-18, 1999, San Diego, CA, USA.Google Scholar
- 5.Valery Guralnik, Jaideep Srivastava: Event Detection from Time Series Data. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’99), pp. 33–42, August 15-18, 1999, San Diego, CA, USA.Google Scholar
- 6.Philip Laird: Identifying and Using Patterns in Sequential Data. In Proceedings of the Fourth International Workshop on Algorithmic Learning Theory (ALT’93), LNCS 744, pp. 1–18, Tokyo, Japan, November 8-10, 1993.Google Scholar