Visualization plays an important role in analyzing and exploring time series data. To facilitate efficient visualization of large datasets, downsampling has emerged as a well-established approach. This work concentrates on LTTB (Largest-Triangle-Three-Buckets), a widely adopted downsampling algorithm for time series data point selection. Specifically, we propose MinMaxLTTB, a two-step algorithm that marks a significant enhancement in the scalability of LTTB. MinMaxLTTB entails the following two steps: (i) the MinMax algorithm preselects a certain ratio of minimum and maximum data points, followed by (ii) applying the LTTB algorithm on only these preselected data points, effectively reducing LTTB's time complexity. The low computational cost of the MinMax algorithm, along with its parallelization capabilities, facilitates efficient preselection of data points. Additionally, the competitive performance of MinMax in terms of visual representativeness also makes it an effective reduction method. Experiments show that MinMaxLTTB outperforms LTTB by more than an order of magnitude in terms of computation time. Furthermore, preselecting a small multiple of the desired output size already provides similar visual representativeness compared to LTTB. In summary, MinMaxLTTB leverages the computational efficiency of MinMax to scale LTTB, without compromising on LTTB's favored visualization properties. The accompanying code and experiments of this paper can be found at https://github.com/predict-idlab/MinMaxLTTB.

翻译：可视化在时间序列数据的分析与探索中具有重要作用。为支持大规模数据集的高效可视化，降采样已成为一种成熟方法。本文聚焦于LTTB（最大三角三桶）算法——一种广泛应用于时间序列数据点选择的降采样算法。具体而言，我们提出MinMaxLTTB双步算法，该算法显著提升了LTTB的可扩展性。MinMaxLTTB包含以下两个步骤：（i）MinMax算法按一定比例预选数据点的最大值与最小值；（ii）仅对这些预选数据点应用LTTB算法，从而有效降低LTTB的时间复杂度。MinMax算法的低计算成本及其并行化能力使其能高效完成数据点预选。此外，MinMax在视觉代表性方面的竞争性表现也使其成为一种有效的降维方法。实验表明，MinMaxLTTB在计算时间上比LTTB提升超过一个数量级。更重要的是，仅需预选目标输出规模的小倍数数据点，即可获得与LTTB相当的视觉代表性。综上所述，MinMaxLTTB通过利用MinMax的计算效率拓展LTTB，同时保持LTTB所具备的优良可视化特性。本文配套代码与实验见 https://github.com/predict-idlab/MinMaxLTTB。