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TimeSeer: Scagnostics for High-Dimensional Time Series
March 2013 (vol. 19 no. 3)
pp. 470-483
Tuan Nhon Dang, Dept. of Comput. Sci., Univ. of Illinois at Chicago, Chicago, IL, USA
A. Anand, Dept. of Comput. Sci., Univ. of Illinois at Chicago, Chicago, IL, USA
L. Wilkinson, Dept. of Comput. Sci., Univ. of Illinois at Chicago, Chicago, IL, USA
We introduce a method (Scagnostic time series) and an application (TimeSeer) for organizing multivariate time series and for guiding interactive exploration through high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional euclidean space. These characterizations include measures, such as, density, skewness, shape, outliers, and texture. Working directly with these Scagnostic measures, we can locate anomalous or interesting subseries for further analysis. Our application is designed to handle the types of doubly multivariate data series that are often found in security, financial, social, and other sectors.
Index Terms:
Time series analysis,Lenses,Shape,Length measurement,Employment,Density measurement,Visualization,multiple time series,Scagnostics,scatterplot matrix,high-dimensional visual analytics
Citation:
Tuan Nhon Dang, A. Anand, L. Wilkinson, "TimeSeer: Scagnostics for High-Dimensional Time Series," IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 3, pp. 470-483, March 2013, doi:10.1109/TVCG.2012.128
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