Visual market sector analysis for financial time series data

VAST 10 - IEEE Conference on Visual Analytics Science and Technology 2010, Proceedings

Volumn , Issue , 2010, Pages 83-90

  Ziegler, Hartmut ^a   Jenny, Marco ^a   Gruse, Tino ^a   Keim, Daniel A ^a  

^a UNIVERSITY OF KONSTANZ   (Germany)

Author keywords

Explorative analysis; Financial information visualization; Time series clustering; Time series data; Visual analytics

Indexed keywords

EXPLORATIVE ANALYSIS; FINANCIAL INFORMATION VISUALIZATION; TIME SERIES CLUSTERING; TIME SERIES DATA; VISUAL ANALYTICS;

CLUSTER ANALYSIS; COMMERCE; DATA VISUALIZATION; FINANCE; FINANCIAL DATA PROCESSING; GRAPHICAL USER INTERFACES; INFORMATION ANALYSIS; INFORMATION SYSTEMS; TIME SERIES; VISUALIZATION;

TIME SERIES ANALYSIS;

EID: 78650929372     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/VAST.2010.5652530     Document Type: Conference Paper

References (49)

1. J. Alsakran, Z. Zhao, X. Zhao, Visual analysis for mutual fund performance, 13th Intl Conf. on Information Visualisation,pp.252-259, 2009
2. W. Aigner, S. Miksch,W. Müller, H. Schumann, and C. Tominski. Visual methods for analyzing time-oriented data. IEEE Transactions on Visualization and Computer Graphics, 14(1),pp. 47 -60, 2008.
3. M. Ankerst, D.A. Keim, H.-P. Kriegel, Circle Segments: A Technique for Visually Exploring Large Multidimensional Data Sets, in Proc. of Visualization 96, Hot Topics Session, 1996
4. E. de Bodt, J. Rynkiewicz, and M. Cottrell. Some known facts about financial data. in Proceedings 9th European Symposium on Artificial Neural Networks (ESANN'01), pp. 223 -236, 2001
5. P. Buono, A. Aris, C. Plaisant, A. Khella, B. Shneiderman, Interactive Pattern Search in Time Series, Proc. of Conference on Visualization and Data Analysis (VDA), SPIE, pp. 175-186, 2005
6. D. Brodbeck, M. Chalmers, A. Lunzer, and P. Cotture. Domesticating bead: adapting an information visualization system to a financial institution. in Proc. IEEE Symp. on Information Visualization, 73-80,1997
7. F.L. Chung, T.C. Fu, R. Luk and V. Ng, Flexible Time Series Pattern Matching Based on Perceptually Important Points, Intl Joint Conference on Artificial Intelligence (IJCAI 2001) Workshop on Learning from Temporal and Spatial Data, pp. 1-7, 2001.
8. C. Csallner, M. Handte, O. Lehmann, and J. Stasko. Fundexplorer: Supporting the diversification of mutual fund portfolios using context treemaps. IEEE Symp. on Information Visualization, 203-208, 2003
9. D.H. Douglas and T.K. Peucker. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. The Canadian Cartographer, 10(2), pp. 112-122, 1973
10. T. Dwyer and D. Gallagher. Visualising changes in fund manager holdings in two and a half-dimensions. in Information Visualization, 3(4), pp.227-244, 2004
11. G. Das, D. Gunopulos, and H. Mannila, Finding Similar Time Series, Proc. of Symp. On Principles of Knowledge Discovery and Data Mining PKDD 1997, pp. 88-100
12. R. Edwards and J. Magee. Technical Analysis of Stock Trends, 2001
13. T. Eklund, B. Back, H. Vanharanta, A. Visa, Assessing the Feasibility of Self-Organizing Maps for Data Mining Financial Information, Xth European Conference on information system, pp.6-8, 2002
14. T. Eklund, B. Back, H. Vanharanta, A. Visa, Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking, Information Visualization Journal, Vol. 2 (3), pp. 161-171, 2003
15. T. Fu, F.-L. Chung, R. Luk, and C.-M. Ng, Financial Time Series Indexing Based on Low Resolution Clustering, 4th IEEE Intl Conference on Data Mining (ICDM 2004) Workshop on Temporal Data Mining: Algorithms, Theory and Applications, pp. 5-14, 2004.
16. T.C. Fu, F.L. Chung, R. Luk, and C.M. Ng, Representing Financial Time Series Based on Data Point Importance, Engineering Applications of Artificial Intelligence, Vol. 21, Issue 2, pp. 277-300, 2008
17. M. Gavrilov, D. Anguelov, P. Indyk, and R. Motwani, Mining The Stock Market: Which Measure Is Best?, in Proc. of the 6th ACM Int'l Conference on Knowledge Discovery and Data Mining (KDD), 2000
18. H. Hochheiser, Interactive Graphical Querying of Time Series and Linear Sequence Data Sets, Ph.D. thesis, University of Maryland,2003
19. K. Kalpakis, D. Gada, and V. Puttagunta, Distance measures for effective clustering of ARIMA time-series. In Proc. of the IEEE Intl. Conf. on Data Mining, ICDM, pp. 273-280, 2001
20. J. Hershberger and J. Snoeyink, Speeding up the Douglas-Peucker line-simplification algorithm, in Proceedings of the 5th Symposium on Data Handling, pp.134-143, 1992
21. D.A. Keim, Pixel-Oriented Visualization Techniques for Exploring Very Large Databases, Journal of Computational and Graphical Statistics, Vol 5, pp. 58-77, 1996
22. D.A. Keim, T. Nietzschmann, N. Schelwies, J. Schneidewind, T. Schreck, and H. Ziegler. A Spectral Visualization System for Analyzing Financial Time Series Data. in Proc. of Eurographics/IEEE-VGTC Symposium on Visualization (EUROVIS'06), pp. 195-200, 2006
23. D.A. Keim, M. Hao, U. Dayal, M. Hsu, J. Ladisch, Pixel Bar Charts: A New Technique for Visualizing Large Multi-Attribute Data Sets without Aggregation, IEEE Symposium on Information Visualization,2001
24. E. Keogh, J. Lin, and W. Truppel, Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research, University of California - Riverside, 2003
25. E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra, Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems, pp. 263-286, 2001
26. E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra, Locally adaptive dimensionality reduction for indexing large time series databases. ACM SIGMOD Conference on Management of Data, 151-162,2001
27. W. Liao, Clutersting of time series data - a survey. Pattern Recognition, Vol. 38, Issue 11, November 2005, pp.1857-1874
28. J. Lin, M. Vlachos, E. Keogh, and D. Gunopulos, Iterative Incremental Clustering of Time Series, Advances in Database Technology - EDBT 2004, 9th Intl. Conference on Extending Database Technology, 2004
29. J. Lin, E. Keogh, S. Lonardi, J.P. Lankford, and D.M. Nystrom, Visually Mining and Monitoring Massive Time Series. in Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 460-469, 2004
30. L. Lin, L. Cao, and C. Zhang, The fish-eye visualization of foreign currency exchange data streams, in Proc. Asia-Pacific Symposium on Information Visualisation (APVis), pp. 91-96, 2005
31. J. MacQueen, Some methods for classification and analysis of multivariate observations, 5th Berkeley Symposium on Mathematical Statistics and Probability, Vol.1, pp.281-297
32. D. Marghescu, Multidimensional Data Visualization Techniques for Financial Performance Data: A Review, TUCS Technical Report No 810, University of Turku, Finland, 2007
33. C. Merino, M. Sips, C. Panse, R. Spence, and D. Keim, Task-at-hand interface for change detection in stock market data. in Proc. ACM Advanced Visual Interfaces Intl Working Conference (AVI), 2006.
34. F. Moerchen, Time Series Knowledge Mining, PhD thesis, 2006
35. J. J. Murphy, editor. Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. 1999.
36. E. Parrish, StockVis: An Internet-Based System for Visualizing Stock Market Data, Master thesis, UC Santa Cruz, 2000
37. P. Roberts, Information Visualization for Stock Market Ticks: Toward a New Trading Interface, Master thesis, M.I.T., 2004
38. C. Ratanamahatana, E. Keogh, T. Bagnall, and Lonardi, A Novel Bit Level Time Series Representation with Implications for Similarity Search and Clustering,In Proc. 9th Pacific-Asian Int. Conf. on Knowledge Discovery and Data Mining (PAKDD05), 2005
39. T. Schreck, T. Tekusova, and J. Kohlhammer, and D. Fellner. Trajectory-based visual analysis of large financial time series data. in ACMSIGKDD Explorations Newsletter, 9(2): pp. 30 -37, 2007
40. K. Šimunič. Visualization of stock market charts. In Proceedings of the International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), 2003
41. http://www.smartmoney.com/map-of-the-market/
42. L. Strausfeld, Financial Viewpoints: using point-of-view to enable understanding of information, in Conference on Human Factors in Computing Systems, pp. 208-209, 1995
43. T. Taskaya and K. Ahmad, Bimodal Visualisation: A Financial Trading Case Study, In Proc. of the 7th International Conference on Information Visualization (IV'03), pp. 320ff, 2003
44. T. Tekušová and J. Kohlhammer. Applying animation to the visual analysis of financial time-dependent data. in Proceedings of the International Conference on Information Visualization (IV), 2007.
45. J. J. van Wijk and E. R. van Selow, Cluster and calendarbased visualization of time series data. In Proceedings of the IEEE Symp on Information Visualization, pp. 4-9, 1999
46. A. Varshney and A. Kaufman. Finesse: a financial information spreadsheet. in Proceedings of the IEEE Symposium on Information Visualization, pp. 70 71, 125, Oct. 1996.
47. M.Wattenberg. Visualizing the stock market. CHI Extended Abstracts on Human Factors in Computing Systems, pp. 188 189, 1999
48. M. Weber, M. Alexa, and W. Muller. Visualizing time-series on spirals. IEEE Symposium on Information Visualization, pages 713, 2001.
49. H. Ziegler, T. Nietzschmann, and D. Keim. Visual analytics on the financial market: Pixel-based analysis and comparison of long-term investments. Intl. Conf. on Information Visualisation, pp.287ff, 20088