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TimeSeriesPaths: Projection-based explorative analysis of multivariate time series data

: Bernard, Jürgen; Wilhelm, Nils; Scherer, Maximilian; May, Thorsten; Schreck, Tobias

Skala, Vaclav (Ed.) ; European Association for Computer Graphics -EUROGRAPHICS-:
WSCG 2012, 20th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. Conference Proceedings : Plzen, Czech Republic, June 26 - 28, 2012
Plzen: Vaclav Skala - Union Agency, 2012 (Journal of WSCG 20.2012, Nr.2)
ISSN: 1213-6972
International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) <20, 2012, Plzen, Czech Republic>
Conference Paper, Journal Article
Fraunhofer IGD ()
Multivariate data; time series data visualization; time series analysis; visual cluster analysis; data exploration; Forschungsgruppe Visual Search and Analysis (VISA); Business Field: Visual decision support; Research Area: Generalized digital documents

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well.

We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth observation, demonstrating the applicability and usefulness of our approach.