Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges
This work addresses visualization challenges for practitioners analyzing time-series data with large value ranges, offering incremental improvements over existing methods.
The paper tackled the problem of visualizing time-series data with large value ranges by introducing two novel designs: the order of magnitude horizon graph and order of magnitude line chart, which split values into mantissa and exponent. In an empirical study, the order of magnitude horizon graph performed better or equal to state-of-the-art visualizations in identification, discrimination, and estimation tasks, with only trend detection favoring traditional horizon graphs.
We introduce two novel visualization designs to support practitioners in performing identification and discrimination tasks on large value ranges (i.e., several orders of magnitude) in time-series data: (1) The order of magnitude horizon graph, which extends the classic horizon graph; and (2) the order of magnitude line chart, which adapts the log-line chart. These new visualization designs visualize large value ranges by explicitly splitting the mantissa m and exponent e of a value v = m * 10e . We evaluate our novel designs against the most relevant state-of-the-art visualizations in an empirical user study. It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection. For each task we analyse error, confidence, and response time. The new order of magnitude horizon graph performs better or equal to all other designs in identification, discrimination, and estimation tasks. Only for trend detection tasks, the more traditional horizon graphs reported better performance. Our results are domain-independent, only requiring time-series data with large value ranges.