¹Computer Science & Engineering Department, University of California, Riverside, CA, U.S.A.

Correspondence: Eamonn Keogh, Computer Science & Engineering Department, University of California, Riverside, CA 92521, U.S.A. Tel: +1 951 827 2032; Fax: +1 951 827 4643; E-mail: eamonn@cs.ucr.edu

^†Dr. Eamonn Keogh is supported by NSF Career Award IIS-0237918.

Received 19 November 2004; Revised 6 January 2005; Accepted 6 January 2005; Published online 7 April 2005.

Abstract

Data visualization techniques are very important for data analysis, since the human eye has been frequently advocated as the ultimate data-mining tool. However, there has been surprisingly little work on visualizing massive time series data sets. To this end, we developed VizTree, a time series pattern discovery and visualization system based on augmenting suffix trees. VizTree visually summarizes both the global and local structures of time series data at the same time. In addition, it provides novel interactive solutions to many pattern discovery problems, including the discovery of frequently occurring patterns (motif discovery), surprising patterns (anomaly detection), and query by content. VizTree works by transforming the time series into a symbolic representation, and encoding the data in a modified suffix tree in which the frequency and other properties of patterns are mapped onto colors and other visual properties. We demonstrate the utility of our system by comparing it with state-of-the-art batch algorithms on several real and synthetic data sets. Based on the tree structure, we further device a coefficient which measures the dissimilarity between any two time series. This coefficient is shown to be competitive with the well-known Euclidean distance.