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Optimization methods for the imputation of missing values in Educational Institutions Data.

D Aureli1, R Bruni2, C Daraio2

  • 1Dep. of Information Engineering, Electronics and Telecommunications, "Sapienza" University of Rome, Rome, Italy.

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|August 26, 2021
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Summary
This summary is machine-generated.

This study introduces novel methods for imputing missing multivariate time series data in educational institutions. The techniques ensure imputed values preserve institutional trends, size, and ratios for accurate data analysis.

Keywords:
Data imputationEducational InstitutionsInformation ReconstructionInterconnected dataMachine learning

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Area of Science:

  • Data Science
  • Educational Data Mining
  • Time Series Analysis

Background:

  • Missing data in educational institution datasets present challenges due to their multivariate and interconnected time-series nature.
  • Existing imputation methods often fail to adequately address the complex interdependencies within institutional data.

Purpose of the Study:

  • To develop and present robust imputation methodologies for multivariate time series data from educational institutions.
  • To address various missing data patterns, including subsequences and full sequences, while preserving data integrity.

Main Methods:

  • Trend smoothing imputation: Utilizes a weighted combination of available data and linear regression to impute time series subsequences, preserving trends.
  • Donor imputation: Handles full missing sequences by selecting and adapting data from similar institutions based on optimization criteria.

Main Results:

  • The proposed methods effectively impute missing data across different patterns in interconnected institutional time series.
  • Imputed values successfully maintain the original trend, size, and internal ratios of the educational institutions.

Conclusions:

  • The developed imputation techniques offer a reliable solution for handling missing data in complex educational datasets.
  • These methods enhance the accuracy and reliability of analyses involving time-series data from educational institutions.