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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Guidance for Interactive Visual Analysis in Multivariate Time Series Preprocessing.

Flor de Luz Palomino Valdivia1, Herwin Alayn Huillcen Baca1

  • 1Faculty of Engineering, Academic Department of Engineering and Information Technology, Jose Maria Arguedas National University, Andahuaylas 03701, Peru.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study introduces GUIAVisWeb, a novel system for interactive visual analysis that guides users through multivariate time series preprocessing. It enhances decision-making quality and explainability, improving analytical workflow efficiency.

Keywords:
explainabilityguidanceinteractive visual analysismultivariate time seriespreprocessingrecommendation

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Area of Science:

  • Data Science
  • Time Series Analysis
  • Human-Computer Interaction

Background:

  • Multivariate time series analysis faces challenges like dynamism, heterogeneity, and scalability.
  • Preprocessing is critical but lacks system support, leading to user errors and cognitive overload.
  • Existing interactive visual analysis lacks integrated guidance for preprocessing.

Purpose of the Study:

  • To design and develop a guidance system for interactive visual analysis in multivariate time series preprocessing.
  • To enable users to understand, evaluate, and adapt preprocessing decisions.
  • To improve the quality, transparency, and effectiveness of the analytical workflow.

Main Methods:

  • Developed GUIAVisWeb, a tool integrating recommendations, explainability, and interactive visualization.
  • Organized workflow into tasks, subtasks, and algorithms.
  • Recommended preprocessing components via consensus validation and predictive evaluation.
  • Provided visual explanations for algorithm recommendations.

Main Results:

  • Guidance quality scored an average of 6.19/7.
  • Explainability of recommendations scored an average of 5.56/6.
  • A case study with air quality data demonstrated the tool's functionality and support for informed decisions.

Conclusions:

  • The GUIAVisWeb tool effectively supports users in multivariate time series preprocessing.
  • The system enhances decision-making transparency and effectiveness.
  • Interactive visual analysis with guidance mechanisms is crucial for complex data preprocessing.