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

2016 ◽  
pp. 3-17 ◽  
Author(s):  
Mike Sips ◽  
Carl Witt ◽  
Tobias Rawald ◽  
Norbert Marwan
Keyword(s):  
Time Series ◽  
Visual Analytics ◽  
Large Sets

scholarly journals Visual Analytics for Correlation-Based Comparison of Time Series Ensembles

2015 ◽  
Vol 34 (3) ◽  
pp. 411-420 ◽  
Author(s):  
P. Köthur ◽  
C. Witt ◽  
M. Sips ◽  
N. Marwan ◽  
S. Schinkel ◽  
...  
Keyword(s):  
Time Series ◽  
Visual Analytics

scholarly journals MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales

2020 ◽  
pp. 1-1
Author(s):  
Philipp Meschenmoser ◽  
Juri F. Buchmuller ◽  
Daniel Seebacher ◽  
Martin Wikelski ◽  
Daniel A. Keim
Keyword(s):  
Time Series ◽  
Visual Analytics ◽  
Multiple Scales

Visual analytics of phosphorylation time-series data on insulin response

2013 ◽  
Author(s):  
David K. G. Ma ◽  
Christian Stolte ◽  
Sandeep Kaur ◽  
Michael Bain ◽  
Seán I. O'Donoghue
Keyword(s):  
Time Series ◽  
Visual Analytics ◽  
Time Series Data ◽  
Insulin Response ◽  
Series Data

scholarly journals A Visual Analytics Approach to Multiscale Exploration of Environmental Time Series

2012 ◽  
Vol 18 (12) ◽  
pp. 2899-2907 ◽  
Author(s):  
Mike Sips ◽  
Patrick Kothur ◽  
Andrea Unger ◽  
Hans-Christian Hege ◽  
Doris Dransch
Keyword(s):  
Time Series ◽  
Visual Analytics ◽  
Environmental Time Series

scholarly journals VAET: A Visual Analytics Approach for E-Transactions Time-Series

2014 ◽  
Vol 20 (12) ◽  
pp. 1743-1752 ◽  
Author(s):  
Cong Xie ◽  
Wei Chen ◽  
Xinxin Huang ◽  
Yueqi Hu ◽  
Scott Barlowe ◽  
...  
Keyword(s):  
Time Series ◽  
Visual Analytics

scholarly journals Interactive visual summaries for detection and assessment of spatiotemporal patterns in geospatial time series

2013 ◽  
Vol 13 (3) ◽  
pp. 283-298 ◽  
Author(s):  
Patrick Köthur ◽  
Mike Sips ◽  
Andrea Unger ◽  
Julian Kuhlmann ◽  
Doris Dransch
Keyword(s):  
Time Series ◽  
Hierarchical Clustering ◽  
Visual Analytics ◽  
Original Data ◽  
Spatiotemporal Patterns ◽  
Spatial And Temporal Variability ◽  
Starting Point ◽  
Study Results ◽  
Temporal Aspects ◽  
Simultaneous Assessment

Numerous measurement devices and computer simulations produce geospatial time series that describe a wide variety of processes of System Earth. A major challenge in the analysis of such data is the complexity of the described processes, which requires a simultaneous assessment of the data’s spatial and temporal variability. To address this task, geoscientists often use automated analyses to compute a compact description of the data, ideally comprising characteristic spatial states of the process under study and their occurrence over time. The results of such automated methods depend on the parameterization, especially the number of extracted spatial states. A particular number of spatial states, however, may only reflect certain spatial or temporal aspects. We introduce a visual analytics approach that overcomes this limitation by allowing users to extract and explore various sets of spatial states to detect characteristic spatiotemporal patterns. To this end, we use the results of hierarchical clustering as a starting point. It groups all time steps of a geospatial time series into a hierarchy of clusters. Users can interactively explore this hierarchy to derive various sets of spatial states. To facilitate detailed inspection of these sets, we employ the concept of interactive visual summaries. A visual summary is the depiction of a set of spatial states and their associated time steps or intervals. It includes interactive means that allow users to assess how well the depicted patterns characterize the original data. Our visual interface comprises a system of visualization components to facilitate both the extraction of sets of spatial states from the hierarchical clustering output and their detailed inspection using interactive visual summaries. This study results from a close collaboration with geoscientists. In an exemplary analysis of observational ocean data, we show how our approach can help geoscientists gain a better understanding of geospatial time series.

scholarly journals A Visual Analytics Platform and Advanced Visualization Tools for Interpreting and Analyzing Wind Energy Time-Series Data

2018 ◽  
Vol 51 (28) ◽  
pp. 480-485 ◽  
Author(s):  
George Chin ◽  
Yousu Chen ◽  
Erin Fitzhenry ◽  
Blaine McGary ◽  
Meg Pirrung ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Energy ◽  
Visual Analytics ◽  
Time Series Data ◽  
Series Data ◽  
Visualization Tools ◽  
Advanced Visualization
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