Conference Papers

Pattern Recognition and Classification for Multivariate Time Series

AuthorStephan Spiegel, Julia Gaebler, Andreas Lommatzsch, Ernesto De Luca, Sahin Albayrak
SourceKDD-2011: Proceeding of ACM International Workshop on Knowledge Discovery from Sensor Data (SensorKDD-2011), San Diego, CA, USA 
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Nowadays we are faced with fast growing and permanently evolving data, including social networks and sensor data recorded from smart phones or vehicles. Temporally evolving data brings a lot of new challenges to the data mining and machine learning community. This paper is concerned with the recognition of recurring patterns within multivariate time series, which capture the evolution of multiple parameters over a certain period of time. Our approach first separates a time series into segments that can be considered as situations, and then clusters the recognized segments into groups of similar context. The time series segmentation is established in a bottom-up manner according the correlation of the individual signals. Recognized segments are grouped in terms of statistical features using agglomerative hierarchical clustering. The proposed approach is evaluated on the basis of real-life sensor data from different vehicles recorded during car drives. According to our evaluation it is feasible to recognize recurring patterns in time series by means of bottom-up segmentation and hierarchical clustering.