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.

通过引入两种新颖的可视化设计，我们支持从业者在时间序列数据的大范围值（即多个数量级）上进行识别和区分任务：（1）数量级视域图扩展经典视域图；（2）数量级折线图调整对数折线图。这些新的可视化设计通过显式地拆分值 v = m * 10e 的尾数 m 和指数 e 来可视化大范围值。在经验用户研究中，我们将我们的新设计与最相关的最先进的可视化进行评估。它关注时间序列和大范围值可视化分析中常用的四个主要任务：识别、区分、估计和趋势检测。对于每个任务，我们分析了错误、置信度和响应时间。新的数量级视域图在识别、区分和估计任务中表现更好或与其他设计相当。只有对于趋势检测任务，传统的视域图报告了更好的性能。我们的结果是领域无关的，只需要具有大范围值的时间序列数据。