Pattern Recognition in Multivariate Time Series

Nowadays computer scientists are faced with fast growing and permanently evolving data, which are represented as observations made sequentially in time. A common problem in the data mining community is the recognition of recurring patterns within temporal databases or streaming data. This dissertation proposal aims at developing and investigating efficient methods for the recognition of contextual patterns in multivariate time series in different application domains based on machine learning techniques. To this end, we propose a generic three-step approach that involves (1) feature extraction to build robust learning models based on significant time series characteristics, (2) segmentation to identify internally homogeneous time intervals and change points, as well as (3) clustering and/or classification to group the time series (segments) into the sub-population to which they belong to. To support our proposed approach, we present and discuss first experiments on real-life vehicular data. Furthermore we describe a number of applications, where pattern recognition in multivariate time series is practical or rather necessary.