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    This study introduces a novel streamgraph visualization to effectively display hierarchical structures in multiple time series data. The approach enhances exploration and comparison of temporal patterns across various granularities.

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    Area of Science:

    • Data Visualization
    • Information Visualization
    • Computer Science

    Background:

    • Multiple time series data are prevalent across diverse fields like medicine, finance, and manufacturing.
    • Streamgraph and ThemeRiver visualizations are common for analyzing temporal patterns but face scalability issues with numerous time series.
    • Hierarchical organization of time series based on proximity can address scalability challenges.

    Purpose of the Study:

    • To present a new streamgraph-based visualization method for conveying hierarchical structures within multiple time series.
    • To facilitate exploration and comparison of temporal evolution patterns in large datasets.
    • To enable analysis at different levels of detail, from overview to specific data points.

    Main Methods:

    • Developed a novel streamgraph visualization technique.
    • Incorporated a focus+context approach for multi-granularity exploration.
    • Organized multiple time series into a hierarchical structure based on proximity.

    Main Results:

    • The proposed method effectively visualizes the hierarchical structure of multiple time series.
    • It allows for exploration and comparison of temporal evolution at various granularities.
    • Two usage examples demonstrate the approach's utility.

    Conclusions:

    • The new streamgraph-based approach improves the scalability and interpretability of multiple time series visualization.
    • It offers a powerful tool for analyzing complex temporal data across different domains.
    • The focus+context technique enhances user interaction and data exploration capabilities.