Název: Timeseriespaths: projection-based explorative analysis of multivarate time series data
Autoři: Bernard, Jürgen
Nils, Wilhelm
Scherer, Maximilian
May, Thorsten
Schreck, Tobias
Citace zdrojového dokumentu: Journal of WSCG. 2012, vol. 20, no. 2, p. 97-106.
Datum vydání: 2012
Nakladatel: Václav Skala - UNION Agency
Typ dokumentu: article
článek
URI: http://wscg.zcu.cz/WSCG2012/!_2012-Journal-Full-2.pdf
http://hdl.handle.net/11025/1070
ISSN: 1213–6972 (hardcopy)
1213–6980 (CD-ROM)
1213–6964 (on-line)
Klíčová slova: vícerozměrné časové řady;vizuální klastrová analýza;průzkumná datová analýza;datová projekce;datová agregace
Klíčová slova v dalším jazyce: multivariate time series;visual cluster analysis;exploratory data analysis;data projection;data aggregation
Abstrakt: The analysis of time-dependent data is an important problem in many application domains, and interactive visual- ization 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, detec- tion 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.
Práva: © Václav Skala - UNION Agency
Vyskytuje se v kolekcích:Number 2 (2012)

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