TiVy: Time Series Visual Summary for Scalable Visualization

• URL: http://arxiv.org/abs/2507.18972v2
• Date: Mon, 28 Jul 2025 23:00:54 GMT
• Title: TiVy: Time Series Visual Summary for Scalable Visualization
• Authors: Gromit Yeuk-Yin Chan, Luis Gustavo Nonato, Themis Palpanas, Cláudio T. Silva, Juliana Freire,
• Abstract summary: We propose TiVy, a new algorithm that summarizes time series using sequential patterns.<n>We also present an interactive time series visualization that renders large-scale time series in real-time.
• Score: 32.33793043326047
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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