IEEE Trans Vis Comput Graph. 2018 Feb;24(2):1141-1154. doi: 10.1109/TVCG.2017.2653106. Epub 2017 Jan 16.
Line graphs are usually considered to be the best choice for visualizing time series data, whereas sometimes also scatter plots are used for showing main trends. So far there are no guidelines that indicate which of these visualization methods better display trends in time series for a given canvas. Assuming that the main information in a time series is its overall trend, we propose an algorithm that automatically picks the visualization method that reveals this trend best. This is achieved by measuring the visual consistency between the trend curve represented by a LOESS fit and the trend described by a scatter plot or a line graph. To measure the consistency between our algorithm and user choices, we performed an empirical study with a series of controlled experiments that show a large correspondence. In a factor analysis we furthermore demonstrate that various visual and data factors have effects on the preference for a certain type of visualization.
折线图通常被认为是可视化时间序列数据的最佳选择，而有时也会使用散点图来显示主要趋势。到目前为止，还没有任何指南表明这些可视化方法中哪一种更能在给定的画布上显示时间序列的趋势。假设时间序列的主要信息是其整体趋势，我们提出了一种算法，该算法可以自动选择最佳显示趋势的可视化方法。这是通过测量 LOESS 拟合所表示的趋势曲线与散点图或折线图所描述的趋势之间的视觉一致性来实现的。为了衡量我们的算法和用户选择之间的一致性，我们进行了一系列对照实验的实证研究，结果显示出很大的一致性。在因子分析中，我们进一步证明了各种视觉和数据因素对某种类型的可视化的偏好有影响。
