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    Area of Science:

    • Data Science
    • Time Series Analysis
    • Visual Analytics

    Background:

    • Analyzing dynamic relationships between time series is crucial for understanding complex systems.
    • Existing methods may struggle with noise and identifying stable patterns over time.

    Purpose of the Study:

    • To develop a visual analytics workflow for detecting stable cross-impact patterns in time series pairs.
    • To provide a scalable and interpretable method for analyzing temporal dynamics.

    Main Methods:

    • A sliding window technique to compute impact measures, including a novel Kendall's tau variant.
    • Interactive Ikat plots for exploring impact distributions and identifying stable cross-impact intervals.
    • Extraction and analysis of temporal and spatial distributions of identified cross-impact events.

    Main Results:

    • The framework successfully identifies stable cross-impact patterns in time series data.
    • Demonstrated ability to isolate robust patterns and analyze their temporal and spatial distributions.
    • Experiments on real-world datasets validate the approach's effectiveness.

    Conclusions:

    • The proposed visual analytics workflow offers a scalable and interpretable approach to analyzing complex temporal dynamics.
    • The method effectively detects stable cross-impact patterns, aiding in the understanding of inter-series relationships.
    • This work contributes to advancements in time series analysis and pattern detection techniques.