Representing Multivariate Data by Optimal Colors to Uncover Events of Interest in Time Series Data

Abstract

In this paper, we present a visualization system for users to study multivariate time series data. They first identify trends or anomalies from a global view and then examine details in a local view. Specifically, we train a neural network to project high-dimensional data to a two dimensional (2D) planar space while retaining global data distances. By aligning the 2D points with a predefined color map, high-dimensional data can be represented by colors. Because perceptual color differentiation may fail to reflect data distance, we optimize perceptual color differentiation on each map region by deformation. The region with large perceptual color differentiation will expand, whereas the region with small differentiation will shrink. Since colors do not occupy any space in visualization, we convey the overview of multivariate time series data by a calendar view. Cells in the view are color-coded to represent multivariate data at different time spans. Users can observe color changes over time to identify events of interest. Afterward, they study details of an event by examining parallel coordinate plots. Cells in the calendar view and the parallel coordinate plots are dynamically linked for users to obtain insights that are barely noticeable in large datasets. The experiment results, comparisons, conducted case studies, and the user study indicate that our visualization system is feasible and effective.