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TimeSeer: Scagnostics for High-Dimensional Time Series

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journal contribution
posted on 22.11.2013 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.

Funding

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

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Publisher Statement

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

Publisher

Institute of Electrical and Electronics Engineers

Language

en_US

issn

1941-0506

Issue date

01/03/2013

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