Trajectory-based visual analysis of large financial time series data

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SCHRECK, Tobias, Tatiana TEKUSOVA, Jörn KOHLHAMMER, Dieter FELLNER, 2007. Trajectory-based visual analysis of large financial time series data. In: ACM SIGKDD Explorations Newsletter. 9(2), pp. 30-37. Available under: doi: 10.1145/1345448.1345454

@article{Schreck2007Traje-17305, title={Trajectory-based visual analysis of large financial time series data}, year={2007}, doi={10.1145/1345448.1345454}, number={2}, volume={9}, journal={ACM SIGKDD Explorations Newsletter}, pages={30--37}, author={Schreck, Tobias and Tekusova, Tatiana and Kohlhammer, Jörn and Fellner, Dieter} }

First publ. in: ACM SIGKDD Explorations Newsletter ; 9 (2007), 2. - pp. 30-37 eng 2012-01-31T12:01:39Z terms-of-use Trajectory-based visual analysis of large financial time series data Kohlhammer, Jörn Fellner, Dieter 2007 Schreck, Tobias 2012-01-31T12:01:39Z Visual Analytics seeks to combine automatic data analysis with visualization and human-computer interaction facilities to solve analysis problems in applications characterized by occurrence of large amounts of complex data. The financial data analysis domain is a promising field for research and application of Visual Analytics technology, as it prototypically involves the analysis of large data volumes in solving complex analysis tasks.<br />We introduce a Visual Analytics system for supporting the analysis of large amounts of financial time-varying indicator data. A system, driven by the idea of extending standard technical chart analysis from one to two-dimensional indicator space, is developed. The system relies on an unsupervised clustering algorithm combined with an appropriately designed movement data visualization technique. Several analytical views on the full market and specific assets are offered for the user to navigate, to explore, and to analyze. The system includes automatic screening of the potentially large visualization space, preselecting possibly interesting candidate data views for presentation to the user. The system is applied to a large data set of time varying 2-D stock market data, demonstrating its effectiveness for visual analysis of financial data. We expect the proposed techniques to be beneficial in other application areas as well. Kohlhammer, Jörn Fellner, Dieter Schreck, Tobias Tekusova, Tatiana Tekusova, Tatiana

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