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TimeSeer: Scagnostics for High-Dimensional Time Series

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journal contribution
posted on 22.11.2013, 00:00 by Tuan Nhon Dang, Anushka Anand, Leland Wilkinson
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 sub-series 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.


This work was supported by NSF/DHS grant DMS-FODAVA- 0808860.


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