TiVy: Time Series Visual Summary for Scalable Visualization

TL;DR

TiVy introduces a scalable visualization algorithm that summarizes large-scale time series using symbolic sequences and sequential pattern grouping, enabling clear, uncluttered visual summaries for extensive data.

Contribution

The paper presents TiVy, a novel algorithm that efficiently summarizes time series with sequential pattern grouping, improving scalability and clarity over existing methods.

Findings

Extracts clear and accurate patterns from large time series data.

Achieves 1000X speed-up compared to straightforward DTW clustering.

Enables exploration of hidden structures in massive time series datasets.

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.