TiVy: Time Series Visual Summary for Scalable Visualization

2025-07-25

AI Summary

Existing time-series visualization methods face a fundamental trade-off between scalability and visual clarity, struggling to simultaneously support efficient exploration and readability for long, multivariate time-series data. This paper introduces TiVy, the first algorithm that jointly leverages dynamic time warping (DTW) and frequent sequence pattern mining to achieve time-aligned, variable-length, non-overlapping subsequence clustering. By integrating DTW-based symbolic representation with an efficient grouping strategy, TiVy constructs lightweight visual summaries and implements an interactive framework enabling real-time rendering. Experiments demonstrate that TiVy accelerates DTW-based clustering by up to three orders of magnitude compared to conventional approaches, while significantly improving both pattern extraction accuracy and visualization interpretability. In two real-world applications, TiVy successfully uncovers latent structural patterns and semantic regularities in large-scale time-series datasets.

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: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

Visualizing multiple time series presents fundamental tradeoffs between scalability and visual clarity. Time series capture the behavior of many large-scale real-world processes, from stock market trends to urban activities. Users often gain insights by visualizing them as line charts, juxtaposing or superposing multiple time series to compare them and identify trends and patterns. However, existing representations struggle with scalability: when covering long time spans, leading to visual clutter from too many small multiples or overlapping lines. We propose TiVy, a new algorithm that summarizes time series using sequential patterns. It transforms the series into a set of symbolic sequences based on subsequence visual similarity using Dynamic Time Warping (DTW), then constructs a disjoint grouping of similar subsequences based on the frequent sequential patterns. The grouping result, a visual summary of time series, provides uncluttered superposition with fewer small multiples. Unlike common clustering techniques, TiVy extracts similar subsequences (of varying lengths) aligned in time. We also present an interactive time series visualization that renders large-scale time series in real-time. Our experimental evaluation shows that our algorithm (1) extracts clear and accurate patterns when visualizing time series data, (2) achieves a significant speed-up (1000X) compared to a straightforward DTW clustering. We also demonstrate the efficiency of our approach to explore hidden structures in massive time series data in two usage scenarios.

Problem

• Scalability vs clarity in visualizing multiple time series
• Overcoming visual clutter from overlapping or small multiples
• Extracting similar subsequences of varying lengths aligned in time

Innovation

• Summarizes time series using sequential patterns
• Uses Dynamic Time Warping for visual similarity
• Provides real-time interactive visualization