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TimeSeriesPaths: Projection-Based Explorative Analysis of Multivariate Time Series Data

Bernard, Jürgen and Wilhelm, Nils and Scherer, Maximilian and May, Thorsten and Schreck, Tobias (2012):
TimeSeriesPaths: Projection-Based Explorative Analysis of Multivariate Time Series Data.
University of West Bohemia, Plzen, In: Journal of WSCG Vol. 20 No. 1-3, 2012. Proceedings, [Conference or Workshop Item]

Abstract

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.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Bernard, Jürgen and Wilhelm, Nils and Scherer, Maximilian and May, Thorsten and Schreck, Tobias
Title: TimeSeriesPaths: Projection-Based Explorative Analysis of Multivariate Time Series Data
Language: English
Abstract:

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.

Publisher: University of West Bohemia, Plzen
Uncontrolled Keywords: Forschungsgruppe Visual Search and Analysis (VISA), Business Field: Visual decision support, Research Area: Generalized digital documents, Multivariate time series, Time series data visualization, Time series analysis, Visual cluster analysis, Data exploration, Data aggregation, Data projection
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Journal of WSCG Vol. 20 No. 1-3, 2012. Proceedings
Date Deposited: 12 Nov 2018 11:16
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