A MULTIVARIATE TIME SERIES CLASSIFICATION METHOD FOR STREAMING DATA USING TEMPORAL METAFEATURE ABSTRACTIONS

In this paper we demonstrate a new approach to the classification of multivariate time series streaming data by utilizing a temporal metafeature abstractions method. The technique extracts global features and metafeatures in order to capture the necessary time-lapse information in the streams of data. The features are then used to create a static, intermediate stream representation that includes all the important time-varying information, and is suitable for analysis using the standard supervised data mining techniques. The capability of the new algorithm called MineTool-TS2 was demonstrated through its application to three datasets: UCSD Microgrid energy usage data, a space physics dataset and synthetic data.