Enhancing Uncertainty Communication in Time Series Predictions: Insights and Recommendations
<|reference_start|>Enhancing Uncertainty Communication in Time Series Predictions: Insights and Recommendations: As the world increasingly relies on mathematical models for forecasts in different areas, effective communication of uncertainty in time series predictions is important for informed decision making. This study explores how users estimate probabilistic uncertainty in time series predictions under different variants of line charts depicting uncertainty. It examines the role of individual characteristics and the influence of user-reported metrics on uncertainty estimations. By addressing these aspects, this paper aims to enhance the understanding of uncertainty visualization and for improving communication in time series forecast visualizations and the design of prediction data dashboards.As the world increasingly relies on mathematical models for forecasts in different areas, effective communication of uncertainty in time series predictions is important for informed decision making. This study explores how users estimate probabilistic uncertainty in time series predictions under different variants of line charts depicting uncertainty. It examines the role of individual characteristics and the influence of user-reported metrics on uncertainty estimations. By addressing these aspects, this paper aims to enhance the understanding of uncertainty visualization and for improving communication in time series forecast visualizations and the design of prediction data dashboards.<|reference_end|>
@article{karagappa2024enhancing,
    title={Enhancing Uncertainty Communication in Time Series Predictions: Insights
  and Recommendations},
    author={Apoorva Karagappa and Pawandeep Kaur Betz and Jonas Gilg and Moritz
  Zeumer and Andreas Gerndt and Bernhard Preim},
    journal={arXiv preprint arXiv:2408.12365},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12365},
    primaryClass={cs.HC cs.LG}
}