TimeSeer: Scagnostics for high-dimensional time series

Tuan Nhon Dang, Anushka Anand, Leland Wilkinson

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

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.

Original languageEnglish
Article number6200267
Pages (from-to)470-483
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Volume19
Issue number3
DOIs
StatePublished - 2013

Keywords

  • Scagnostics
  • high-dimensional visual analytics
  • multiple time series
  • scatterplot matrix

Fingerprint Dive into the research topics of 'TimeSeer: Scagnostics for high-dimensional time series'. Together they form a unique fingerprint.

  • Cite this