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arXiv · 2309.08018

The Effect of Smoothing on the Interpretation of Time Series Data: A COVID-19 Case Study

Oded Stein·Alec Jacobson·Fanny Chevalier
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Abstract

We conduct a controlled crowd-sourced experiment of COVID-19 case data visualization to study if and how different plotting methods, time windows, and the nature of the data influence people's interpretation of real-world COVID-19 data and people's prediction of how the data will evolve in the future. We find that a 7-day backward average smoothed line successfully reduces the distraction of periodic data patterns compared to just unsmoothed bar data. Additionally, we find that the presence of a smoothed line helps readers form a consensus on how the data will evolve in the future. We also find that the fixed 7-day smoothing window size leads to different amounts of perceived recurring patterns in the data depending on the time period plotted -- this suggests that varying the smoothing window size together with the plot window size might be a promising strategy to influence the perception of spurious patterns in the plot.

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Oded Stein, Alec Jacobson, Fanny Chevalier. 2023-09-14. The Effect of Smoothing on the Interpretation of Time Series Data: A COVID-19 Case Study. https://arxiv.org/abs/2309.08018

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