Applying Animation to the Visual Analysis of Financial Time-Dependent Data: Difference between revisions
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*[[Tekušová, Tatiana | Tatiana Tekušová]] | *[[Tekušová, Tatiana | Tatiana Tekušová]] | ||
*[[Kohlhammer, Jörn | Jörn Kohlhammer]] | *[[Kohlhammer, Jörn | Jörn Kohlhammer]] | ||
== Abstract == | |||
{{Quotation|For decades, financial analysts have strived to use modern data visualization tools to improve the timeliness and quality of their analysis. As the amount of data to be processed increases rapidly and requirements on quality of financial analysis rise, the demand for analysis support systems grows. We present a system for the visual analysis of large amounts of time-dependent data using animation. For each data entity, indicators are presented in a scatter-plot framework, displaying the correlation between them. The design of the glyphs illustrates additional data dimensions. The system uses animation to handle the time-dimension of the data. It offers various features, such as focus, zoom, details on demand and time period selection to support the analysis. Financial indicators are used to demonstrate the usability of the system. The animation proves to be a powerful tool for analysing time-dependent processes in cross-sectional data sets and discovering patterns in the data.| [Tekusova and Kohlhammer, 2007]}} | |||
[[category: techniques]] | [[category: techniques]] | ||
Revision as of 21:55, 8 May 2008
Authors
Abstract
For decades, financial analysts have strived to use modern data visualization tools to improve the timeliness and quality of their analysis. As the amount of data to be processed increases rapidly and requirements on quality of financial analysis rise, the demand for analysis support systems grows. We present a system for the visual analysis of large amounts of time-dependent data using animation. For each data entity, indicators are presented in a scatter-plot framework, displaying the correlation between them. The design of the glyphs illustrates additional data dimensions. The system uses animation to handle the time-dimension of the data. It offers various features, such as focus, zoom, details on demand and time period selection to support the analysis. Financial indicators are used to demonstrate the usability of the system. The animation proves to be a powerful tool for analysing time-dependent processes in cross-sectional data sets and discovering patterns in the data.
[Tekusova and Kohlhammer, 2007]