VARTS: A Tool for the Visualization and Analysis of Representative Time Series Data

2026-01-04

arXiv.org

AI Summary

This work proposes an interactive visual analytics approach to address the challenges of visual clutter and redundancy in large-scale time series visualization, which often obscure critical trends. By integrating M4 sampling, dynamic time warping (DTW) similarity computation, and a greedy selection strategy, the method automatically identifies a representative subset of time series that preserves essential patterns while minimizing redundancy. A coordinated multi-view visualization framework further enables users to efficiently explore and interpret the data. The proposed technique significantly enhances visual clarity, interpretability, and analytical efficiency without sacrificing the core temporal characteristics of the original dataset.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Web data visualization
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

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.

Problem

• time series visualization
• visual clutter
• redundant patterns
• temporal trends
• representative time series

Innovation

• representative time series
• M4 sampling
• DTW similarity
• greedy selection
• visual analytics