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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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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to...
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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Related Experiment Video

Updated: Jan 16, 2026

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.