Association Rules Discovery in Multivariate Time Series

A problem of association rules discovery in a multivariate time series is considered in this paper. A method for finding interpretable association rules between frequent qualitative patterns is proposed. A pattern is defined as a sequence of mixed states. The multivariate time series is transformed into a set of labeled intervals and mined for frequently occurring patterns. Then these patterns are analyzed to find out which of them occur close to each other frequently. Some modifications and improvements of the method are proposed and discussed.

• Type: Conference
• Year: 2007
• Where: SYRCODIS
• Authors: Elena Lutsiv
• Keywords: Association Rules, Frequent Qualitative Patterns, Multivariate Time Series, SYRCODIS 2007, SYRCODIS 2008