Multi-Granular Trend Detection for Time-Series Analysis

Abstract

Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data sets. Trend detection is an effective way to simplify time-varying data and to summarize salient information for visual display and interactive analysis. We propose a geometric model for trend-detection in one-dimensional time-varying data, inspired by topological grouping structures for moving objects in two- or higher-dimensional space. Our model gives provable guarantees on the trends detected and uses three natural parameters: granularity, support-size, and duration. These parameters can be changed on-demand. Our system also supports a variety of selection brushes and a time-sweep to facilitate refined searches and interactive visualization of (sub-)trends. We explore different visual styles and interactions through which trends, their persistence, and evolution can be explored.

Corresponding Publications

  • Multi-Granular Trend Detection for Time-Series Analysis

    Arthur van Goethem, Frank Staals, Maarten Lรถffler, Jason Dykes, Bettina Speckmann

    IEEE Transactions on Visualization and Computer Graphics, 2016
    Presented at InfoVis 2016.
    @article{ensembles2016,
      author = {van Goethem, Arthur and Staals, Frank and L{\"o}ffler, Maarten
                     and Dykes, Jason and Speckmann, Bettina},
      title = {Multi-Granular Trend Detection for Time-Series Analysis},
      journal = {IEEE Transactions on Visualization and Computer Graphics},
      volume = {23},
      number = {1},
      pages = {661--670},
      doi = {10.1109/TVCG.2016.2598619},
      issn = {1077-2626},
      publisher = {IEEE},
      year = {2016},
      location = {Baltimore, Maryland, USA},
      numpages = {10},
      keywords = {ensemble, time-series, trend detection, trajectories, grouping,
                     visualization},
      category = {visualization},
      doi = {10.1109/TVCG.2016.2598619},
      url = {https://dx.doi.org/10.1109/TVCG.2016.2598619},
      note = {Presented at InfoVis 2016.},
    }