Time‐Series Plots Integrated in Parallel‐Coordinates Displays

Abstract

Figure 1: A pseudo-perspective view of two time-series plots is integrated between two adjacent axes of a parallel-coordinates display. An independent translucent parallel-coordinates panel connects the two time-series plots and allows the analysis of trends in time as well as the exploration of changes in the relationship between the two time-dependent attributes. AbstractWe present a natural extension of two-dimensional parallel-coordinates plots for revealing relationships in time-dependent multi-attribute data by building on the idea that time can be considered as the third dimension. A time slice through the visualization represents a certain point in time and can be viewed as a regular parallel-coordinates display. A vertical slice through one of the axes of the parallel-coordinates display would show a time-series plot. For a focus-and-context integration of both views, we embed time-series plots between two adjacent axes of the parallel-coordinates plot. Both time-series plots are drawn using a pseudo three-dimensional perspective with a single vanishing point. An independent parallel-coordinates panel that connects the two perspectively displayed time-series plots can move forward and backward in time to reveal changes in the relationship between the time-dependent attributes. The visualization of time-series plots in the context of the parallelcoordinates plot facilitates the exploration of time-related aspects of the data without the need to switch to a separate display. We provide a consistent set of tools for selecting and contrasting subsets of the data, which are important for various application domains.

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

“…iii. handling of time series, e.g., using the third dimension to display temporal evolutions (Gruendl et al, 2016), by plotting several time steps on adjacent axes (Franken, 2009) or by animating a series of charts for each time-step (Heinrich and Weiskopf, 2013), iv. dynamic rescaling of axes, to allow brushing beyond the visible values (currently brushing beyond the visible axis requires manually editing the numerical brush bound in an ad hoc table).…”

Visual analytics for nonlinear programming in robot motion planning

“…These, however, provide a poor representation of the temporal context information and are prone to become cluttered. Other approaches use depth to encode the temporal information, resulting in 3D parallel coordinates (Wegenkittl et al 1997;Gruendl et al 2016), 3D star coordinates (Noirhomme-Fraiture 2002), or space-time cubes (Bach et al 2014(Bach et al , 2015. These methods, however, have scalability concerns regarding the size of the data.…”

Smart Brushing for Parallel Coordinates

“…Gruendl et al [14] use parallel coordinates as an alternative to time series plots. Tory et al present a parallel coordinates interface for exploring volumetric datasets [15].…”