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Towards Visual Analytics for the Exploration of Large Sets of Time Series

Authors
/persons/resource/sips

Sips,  M.
1.5 Geoinformatics, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/cwitt

Witt,  Carl
1.5 Geoinformatics, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/trawald

Rawald,  Tobias
1.5 Geoinformatics, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Marwan,  N.
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Citation

Sips, M., Witt, C., Rawald, T., Marwan, N. (2016): Towards Visual Analytics for the Exploration of Large Sets of Time Series. - In: Webber, Jr., C. L., Ioana, C., Marwan, N. (Eds.), Recurrence Plots and their Quantifications: Expanding Horizons; Proceedings of the 6th International Symposium on Recurrence Plots, Grenoble, France, 17-19 June 2015, (Springer Proceedings in Physics ; 180), Springer International Publishing.
https://doi.org/10.1007/978-3-319-29922-8_1


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_1504108
Abstract
In this paper, we discuss the scientific whether and clustering of time series based on RQA measures leads to an interpretable clustering structure when analyzed by human experts. We are not aware of studies answering this scientific question. Answering it is the crucial first step in the development of a Visual Analytics approach that support users to explore large sets of time series.