Temporal scatterplots

Abstract

Visualizing high-dimensional data on a 2D canvas is generally challenging. It becomes significantly more difficult when multiple time-steps are to be presented, as the visual clutter quickly increases. Moreover, the challenge to perceive the significant temporal evolution is even greater. In this paper, we present a method to plot temporal high-dimensional data in a static scatterplot; it uses the established PCA technique to project data from multiple time-steps. The key idea is to extend each individual displacement prior to applying PCA, so as to skew the projection process, and to set a projection plane that balances the directions of temporal change and spatial variance. We present numerous examples and various visual cues to highlight the data trajectories, and demonstrate the effectiveness of the method for visualizing temporal data.

A Visual Analytics Interface for Formulating Evaluation Metrics of Multi-Dimensional Time-Series Data

“…Projection methods [42] are often employed to mitigate that di culty [35]. As for the visualization proposed by Patashnik et al [43], the characteristics of time-series data (such as burst) are emphasized in preprocessing and the result of dimensionality reduction for each time point is superimposed in a single static view. A trajectory is used to visualize a subset of the data that have large changes in the view.…”