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：我们提出两种新型可视化设计，以支持实践者在时间序列数据中执行大数值范围（即跨越数个数量级）的识别与区分任务：（1）数量级地平线图，它扩展了经典的地平线图；（2）数量级折线图，它改进了对数折线图。这些新型可视化设计通过显式分离数值 v = m * 10^e 的尾数 m 与指数 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，将所提出的设计与最相关的现有前沿可视化方案进行了对比评估。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主要任务：识别、区分、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。在识别、区分和估计任务中，新型的数量级地平线图性能优于或等同于所有其他设计方案。仅在趋势检测任务中，更传统的地平线图报告了更优的性能。我们的研究结果具有领域独立性，仅要求数据为包含大数值范围的时间序列数据。