# Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data

arXiv: https://arxiv.org/abs/2402.03116 · HTML: https://mlsanity.com/paper/2402.03116/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Scientific Machine Learning Benchmarks](https://mlsanity.com/paper/2110.12773/) (2021)
- [Calliope: Automatic Visual Data Story Generation from a Spreadsheet](https://mlsanity.com/paper/2010.09975/) (2020)
- [Machine Learning and Big Scientific Data](https://mlsanity.com/paper/1910.07631/) (2019)
- [Data Player: Automatic Generation of Data Videos with Narration-Animation Interplay](https://mlsanity.com/paper/2308.04703/) (2023)
- [Characterizing Automated Data Insights](https://mlsanity.com/paper/2008.13060/) (2020)
- [MultiVision: Designing Analytical Dashboards with Deep Learning Based Recommendation](https://mlsanity.com/paper/2107.07823/) (2021)
- [PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning](https://mlsanity.com/paper/2009.00249/) (2020)
- [Dashboard Design Patterns](https://mlsanity.com/paper/2205.00757/) (2022)
- [What Makes a Data-GIF Understandable?](https://mlsanity.com/paper/2008.07227/) (2020)
- [Narvis: Authoring Narrative Slideshows for Introducing Data Visualization Designs](https://mlsanity.com/paper/1907.05609/) (2019)
- [Propagating Visual Designs to Numerous Plots and Dashboards](https://mlsanity.com/paper/2107.08882/) (2021)
- [Explanatory Journeys: Visualising to Understand and Explain Administrative Justice Paths of Redress](https://mlsanity.com/paper/2107.14013/) (2021)
- [Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations](https://mlsanity.com/paper/2204.06946/) (2022)

## Abstract (arXiv)

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.