Multi-Granular Trend Detection for Time-Series Analysis

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Arthur Van Goethem, Technische Universiteit Eindhoven
  • ,
  • Frank Staals
  • ,
  • Maarten Löffler, Utrecht University
  • ,
  • Jason Dykes, City University London
  • ,
  • Bettina Speckmann, Technische Universiteit Eindhoven

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.

Original languageEnglish
Article number7536203
JournalIEEE Transactions on Visualization and Computer Graphics
Pages (from-to)661-670
Number of pages10
Publication statusPublished - 1 Jan 2017

    Research areas

  • Interactive Exploration, Time Series, Trend Detection

See relations at Aarhus University Citationformats

ID: 108792564