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.

翻译：本文提出了两种新颖的可视化设计方案，以支持从业人员在大数值范围（即跨越多个数量级）的时间序列数据上执行识别与辨别任务：（1）数量级地平线图，拓展了经典地平线图；（2）数量级折线图，改进自对数折线图。这些新型可视化设计通过显式分离数值 v = m × 10ᵉ 中的尾数 m 和指数 e，实现了对大数值范围的可视化。我们通过一项实证用户研究，将新设计方案与最相关的现有可视化方法进行了评估。研究聚焦于时间序列与大数值范围可视化分析中常用的四项核心任务：识别、辨别、估计和趋势检测。针对每项任务，我们分析了误差、置信度和响应时间。实验结果表明：在识别、辨别和估计任务中，新型数量级地平线图的表现优于或等同于所有其他设计方案；仅在趋势检测任务中，传统地平线图展现出更优性能。本研究结果具有领域无关性，仅需具备大数值范围的时间序列数据即可适用。