STAC: Enhancing stacked graphs for time series analysis

Stacked graphs have been widely used to represent multiple time series simultaneously to show the changes of individual values and their aggregation over time. However, when the number of time series becomes very large, the layers representing time series with small values take up only very small proportions in the stacked graph, making them hard to trace. As a result, it is challenging for analysts to detect the correlation of individual layers and their aggregation, and find trend similarities and differences between layers solely with stacked graphs. In this paper, we study the correlations of individual layers, and their aggregation in time series data presented with stacked graphs, focusing on the local regions within any given time intervals. Specifically, we present STAC, an interactive visual analytics system, to help analysts gain insights into the correlations in stacked graphs. While preserving the original stacked shape, we further link a stacked graph with auxiliary views to facilitate the in-depth analysis of correlations in time series data. A case study based on a real-world dataset demonstrates the effectiveness of our system in gaining insights into time series data analysis and facilitating various analytical tasks.