Visual Exploration of Time-Series Data with Shape Space Projections

Time-series data is a common target for visual analytics, as they appear in a wide range of application domains. Typical tasks in analyzing time-series data include identifying cyclic behavior, outliers, trends, and periods of time that share distinctive shape characteristics. Many methods for visualizing time series data exist, generally mapping the data values to positions or colors. While each can be used to perform a subset of the above tasks, none to date is a complete solution. In this paper we present a novel approach to time-series data visualization, namely creating multivariate data records out of short subsequences of the data and then using multivariate visualization methods to display and explore the data in the resulting shape space. We borrow ideas from text analysis, where the use of N-grams is a common approach to decomposing and processing unstructured text. By mapping each temporal N-gram to a glyph, and then positioning the glyphs via PCA (basically a projection in...

• Type: Journal
• Year: 2011
• Where: CGF
• Authors: Matthew O. Ward, Zhenyu Guo
• Keywords: CGF 2011, Computational Geometry, Graphical User Interfaces, Shape Characteristics, Time Series Data