CHI '86 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The visual display of quantitative information
The visual display of quantitative information
Visualizing Time-Series on Spirals
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
Visualizing and discovering non-trivial patterns in large time series databases
Information Visualization
Visual Methods for Analyzing Time-Oriented Data
IEEE Transactions on Visualization and Computer Graphics
Stacked Graphs – Geometry & Aesthetics
IEEE Transactions on Visualization and Computer Graphics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Graphical Perception of Multiple Time Series
IEEE Transactions on Visualization and Computer Graphics
Narrative Visualization: Telling Stories with Data
IEEE Transactions on Visualization and Computer Graphics
KronoMiner: using multi-foci navigation for the visual exploration of time-series data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
IEEE Transactions on Visualization and Computer Graphics
A Study on Dual-Scale Data Charts
IEEE Transactions on Visualization and Computer Graphics
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Time series data is pervasive in many domains and interactive visualization of such data is useful for a wide range of tasks including analysis and prediction. In spite of the importance of visualizing time series data and the fact that time series data is often easily interpretable, traditional approaches are either very simple and limited, or are aimed at domain experts. In this paper, we propose a novel interactive visualization paradigm for exploring and comparing multiple sets of time series data. In particular, we propose a focus+context approach, where a "focus" segment of a time series is zoomed into and visualized using a linear layout at one scale, while the remaining segments of the time series (i.e., the context) are visualized using spiral data layouts. Our paradigm allows the user to dynamically select and compare different sections of each time series independently, facilitating the exploration of time series data in a fun and engaging way.