TimeCluster: dimension reduction applied to temporal data for visual analytics

Mohammed Ali, Mark W. Jones, Xianghua Xie, Mark Williams

    Research output: Contribution to journalArticlepeer-review

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    Abstract

    There is a need for solutions which assist users to understand long time-series data by observing its changes over time,
    finding repeated patterns, detecting outliers, and effectively labeling data instances. Although these tasks are quite distinct
    and are usually tackled separately, we present an interactive visual analytics system and approach that can address these
    issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series
    data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and
    outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power
    of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and
    easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series
    datasets and report two real-world case studies that are used to evaluate our system.
    Original languageEnglish
    Number of pages14
    JournalVisual computer
    DOIs
    Publication statusPublished - 9 May 2019

    Keywords

    • Time-series data
    • Visual analytics
    • Sliding window
    • Dimension reduction
    • Time-series graph
    • 2D projection
    • Repeated patterns
    • Outliers
    • Labeling

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