  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.
