Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges

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
$v=m\cdot 10^{e}$
. 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 analyze 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.