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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Related Experiment Video

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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Visual analytics for model selection in time series analysis.

Markus Bögl1, Wolfgang Aigner, Peter Filzmoser

  • 1Vienna University of Technology.

IEEE Transactions on Visualization and Computer Graphics
|September 21, 2013
PubMed
Summary
This summary is machine-generated.

Model selection for time series analysis is complex. The TiMoVA prototype offers a visual analytics approach, combining human judgment with computation to aid domain experts in choosing appropriate models.

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Area of Science:

  • Data Science
  • Statistics
  • Human-Computer Interaction

Background:

  • Model selection in time series analysis is challenging for domain experts across various fields.
  • Existing statistical software lacks interactive visual interfaces to combine human judgment with automated computation.
  • This gap hinders effective model selection in complex data analysis tasks.

Purpose of the Study:

  • To propose a Visual Analytics process to assist domain experts in time series model selection.
  • To develop and evaluate a prototype tool, TiMoVA, that implements this process.
  • To enhance the user experience and efficiency of model selection through interactive visualization.

Main Methods:

  • Developed the TiMoVA prototype based on user stories and iterative expert feedback.
  • Implemented a Visual Analytics process integrating human judgment and computational analysis.
  • Evaluated the prototype using usage scenarios with an epidemiological dataset and expert interviews.

Main Results:

  • The TiMoVA prototype effectively supports domain experts in time series model selection.
  • Interactive visual interfaces provided short feedback cycles, improving the selection process.
  • Expert feedback and usage scenarios validated the tool's utility and usability.

Conclusions:

  • Visual analytics offers a promising approach to address challenges in time series model selection.
  • The TiMoVA prototype demonstrates the potential of interactive tools in supporting domain experts.
  • Future work can further refine visual interfaces for enhanced model selection support.