# TiVy: Time Series Visual Summary for Scalable Visualization

arXiv: https://arxiv.org/abs/2507.18972 · HTML: https://mlsanity.com/paper/2507.18972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 84 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [SAX Navigator: Time Series Exploration through Hierarchical Clustering](https://mlsanity.com/paper/1908.05505/) (2019)
- [The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art](https://mlsanity.com/paper/2006.11454/) (2020)
- [Computing Continuous Dynamic Time Warping of Time Series in Polynomial Time](https://mlsanity.com/paper/2203.04531/) (2022)
- [Visualizing a Million Time Series with the Density Line Chart](https://mlsanity.com/paper/1808.06019/) (2018)
- [Early Abandoning PrunedDTW and its application to similarity search](https://mlsanity.com/paper/2010.05371/) (2020)
- [Subtrajectory Clustering: Finding Set Covers for Set Systems of Subcurves](https://mlsanity.com/paper/2103.06040/) (2021)
- [Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges](https://mlsanity.com/paper/2307.10278/) (2023)
- [Efficient Greedy Discrete Subtrajectory Clustering](https://mlsanity.com/paper/2503.14115/) (2025)

## Abstract (arXiv)

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.