Large-scale time series visualization often suffers from excessive visual clutter and redundant patterns, making it difficult for users to understand the main temporal trends. To address this challenge, we present VARTS, an interactive visual analytics tool for representative time series selection and visualization. Building upon our previous work M4-Greedy, VARTS integrates M4-based sampling, DTW-based similarity computation, and greedy selection into a unified workflow for the identification and visualization of representative series. The tool provides a responsive graphical interface that allows users to import time series datasets, perform representative selection, and visualize both raw and reduced data through multiple coordinated views. By reducing redundancy while preserving essential data patterns, VARTS effectively enhances visual clarity and interpretability for large-scale time series analysis. The demo video is available at https://youtu.be/mS9f12Rf0jo.

翻译：大规模时间序列可视化常因视觉杂乱与模式冗余而难以呈现主要时序趋势。为解决此问题，本文提出VARTS——一个面向代表性时间序列选取与可视化的交互式可视分析工具。该工具基于我们前期研究M4-Greedy，将M4采样、基于DTW的相似度计算与贪婪选择算法整合为统一工作流，以实现代表性序列的识别与可视化。系统提供响应式图形界面，支持用户导入时间序列数据集、执行代表性选择，并通过多视图联动同时展示原始数据与降维结果。通过在保留关键数据模式的同时减少冗余，VARTS显著提升了大尺度时间序列分析的视觉清晰度与可解释性。演示视频详见：https://youtu.be/mS9f12Rf0jo。