TimeSeer: Scagnostics for High-Dimensional Time Series

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 se...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2013-03, Vol.19 (3), p.470-483
Main Authors: Tuan Nhon Dang, Anand, A., Wilkinson, L.
Format: Article
Language:English
Subjects:
Algorithms
Computer Graphics
Computer Simulation
Density measurement
Employment
high-dimensional visual analytics
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Length measurement
Lenses
Models, Statistical
multiple time series
Multivariate Analysis
Reproducibility of Results
Scagnostics
scatterplot matrix
Sensitivity and Specificity
Shape
Software
Time series analysis
User-Computer Interface
Visualization
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Description
Summary: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.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2012.128