We present a method to create storytelling visualization with time series data. Many personal decisions nowadays rely on access to dynamic data regularly, as we have seen during the COVID-19 pandemic. It is thus desirable to construct storytelling visualization for dynamic data that is selected by an individual for a specific context. Because of the need to tell data-dependent stories, predefined storyboards based on known data cannot accommodate dynamic data easily nor scale up to many different individuals and contexts. Motivated initially by the need to communicate time series data during the COVID-19 pandemic, we developed a novel computer-assisted method for meta-authoring of stories, which enables the design of storyboards that include feature-action patterns in anticipation of potential features that may appear in dynamically arrived or selected data. In addition to meta-storyboards involving COVID-19 data, we also present storyboards for telling stories about progress in a machine learning workflow. Our approach is complementary to traditional methods for authoring storytelling visualization, and provides an efficient means to construct data-dependent storyboards for different data-streams of similar contexts.

翻译：本文提出了一种利用时间序列数据创建故事性可视化的方法。目前，许多个人决策日益依赖于对动态数据的定期访问，这一点在COVID-19疫情期间尤为明显。因此，为个体根据特定上下文选择的动态数据构建故事性可视化具有重要意义。由于需要讲述依赖于数据的故事，基于已知数据的预定义故事板难以灵活适配动态数据，也难以扩展至多种不同的个体和上下文场景。受COVID-19疫情期间沟通时间序列数据需求的启发，我们开发了一种新颖的计算机辅助方法，用于故事的元创作，其允许设计包含特征-动作模式的故事板，以预判动态到达或选择的数据中可能出现的潜在特征。除涉及COVID-19数据的元故事板外，我们还展示了用于讲述机器学习工作流进展的故事板。该方法是对传统故事性可视化创作方法的补充，并为相似上下文中不同数据流构建依赖数据的故事板提供了一种高效途径。