How To Simplify A Line Chart: A Visual Feature Based Taxonomy And Evaluation

Simplifying large or noisy time-series is often a prerequisite for producing interpretable line charts, yet existing methods vary widely in how they alter emph{visual features} such as trends, extrema, periodicity, curvature, roughness, and overall complexity. We introduce a feature-based taxonomy of line chart simplification that evaluates 19 commonly used algorithms across three families: Transformers, Reducers, and Aggregators. We also formalize 12 perceptually motivated, computationally defined visual features commonly used when analyzing time-series in line charts. We assess preservation using multiple similarity metrics per feature to capture different aspects of fidelity, yielding 21 distinct feature-preservation measurements. To capture the trade-off between fidelity and simplicity, we introduce a emph{Feature–Complexity (FC)} score that combines feature preservation with complexity. Our systematic evaluation on 80 datasets reveals distinct family-level patterns: Transformers such as Gaussian, Mean, and Median filters achieve A-mode grades on nearly all metrics but retain full temporal resolution; Reducers such as Douglas-Peucker and M4 receive predominantly D-F mode grades, trading fidelity for a reduction in the number of rendered points; and Aggregators ASAP and PAA occupy a middle ground with primarily B-C mode grades. We further characterize the predictability of algorithm behavior across datasets: some methods produce consistent grades regardless of dataset type (Gaussian, Mean, Douglas-Peucker), while others exhibit high data-dependency, with performance varying substantially based on dataset characteristics (FPCS, ASAP). An interactive website supporting exploration of all evaluated techniques, datasets, and results is available at url{https://feature-taxonomy.vercel.app/}.

Rifat Ara Proma, and Paul Rosen. How To Simplify A Line Chart: A Visual Feature Based Taxonomy And Evaluation. IEEE Transactions on Visualization and Computer Graphics, 2026.

@article{proma2026line,
  title = {How to Simplify a Line Chart: A Visual Feature Based Taxonomy and Evaluation},
  author = {Proma, Rifat Ara and Rosen, Paul},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  year = {2026},
  abstract = { Simplifying large or noisy time-series is often a prerequisite for
    producing interpretable line charts, yet existing methods vary widely in how they alter
    emph{visual features} such as trends, extrema, periodicity, curvature, roughness, and
    overall complexity. We introduce a feature-based taxonomy of line chart simplification
    that evaluates 19 commonly used algorithms across three families: Transformers,
    Reducers, and Aggregators. We also formalize 12 perceptually motivated, computationally
    defined visual features commonly used when analyzing time-series in line charts. We
    assess preservation using multiple similarity metrics per feature to capture different
    aspects of fidelity, yielding 21 distinct feature-preservation measurements. To capture
    the trade-off between fidelity and simplicity, we introduce a emph{Feature--Complexity
    (FC)} score that combines feature preservation with complexity. Our systematic
    evaluation on 80 datasets reveals distinct family-level patterns: Transformers such as
    Gaussian, Mean, and Median filters achieve A-mode grades on nearly all metrics but
    retain full temporal resolution; Reducers such as Douglas-Peucker and M4 receive
    predominantly D-F mode grades, trading fidelity for a reduction in the number of
    rendered points; and Aggregators ASAP and PAA occupy a middle ground with primarily B-C
    mode grades. We further characterize the predictability of algorithm behavior across
    datasets: some methods produce consistent grades regardless of dataset type (Gaussian,
    Mean, Douglas-Peucker), while others exhibit high data-dependency, with performance
    varying substantially based on dataset characteristics (FPCS, ASAP). An interactive
    website supporting exploration of all evaluated techniques, datasets, and results is
    available at url{https://feature-taxonomy.vercel.app/}. }
}