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Trajectory-Based Visual Analysis of Large Financial Time Series Data

Schreck, Tobias and Tekusová, Tatiana and Kohlhammer, Jörn and Fellner, Dieter W. (2007):
Trajectory-Based Visual Analysis of Large Financial Time Series Data.
In: ACM SIGKDD Explorations Newsletter, pp. 30-37, 9, (2), [Article]

Abstract

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. 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.

Item Type: Article
Erschienen: 2007
Creators: Schreck, Tobias and Tekusová, Tatiana and Kohlhammer, Jörn and Fellner, Dieter W.
Title: Trajectory-Based Visual Analysis of Large Financial Time Series Data
Language: English
Abstract:

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. 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.

Journal or Publication Title: ACM SIGKDD Explorations Newsletter
Volume: 9
Number: 2
Uncontrolled Keywords: Forschungsgruppe Visual Search and Analysis (VISA), Forschungsgruppe Semantic Models, Immersive Systems (SMIS), Visual analytics, Financial data, Time series data visualization, Trajectory clustering
Divisions: UNSPECIFIED
20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 16 Apr 2018 09:03
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