Paper Info
Title
Visual exploration of time-series data with shape space projections
Abstract
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 shape space), many different kinds of patterns in the sequence can be readily identified. Interactive selection via brushing, in conjunction with linking to other visualizations, provides a wide range of tools for exploring the data. We validate the usefulness of this approach with examples from several application domains and tasks, comparing our methods with traditional time-series visualizations.
Year
DOI
Venue
2011
10.1111/j.1467-8659.2011.01919.x
Comput. Graph. Forum
Keywords
Field
DocType
application domain,novel approach,shape space projection,time-series data,time-series data visualization,multivariate data record,resulting shape space,data value,wide range,visualizing time series data,common approach,visual exploration,graphical user interfaces
Glyph,Computer vision,Time series,Data mining,Data visualization,Computer science,Multivariate statistics,Visual analytics,Outlier,Graphical user interface,Artificial intelligence,User interface
Journal
Volume
Issue
ISSN
30
3
0167-7055
Citations 
PageRank 
References 
12
0.57
23
Authors
2
Name
Order
Citations
PageRank
Matthew O. Ward11757189.48
Zhenyu Guo251239.61