A Method of Pruning and Random Replacing of Known Values for Comparing Missing Data Imputation Models for Incomplete Air Quality Time Series

A Method of Pruning and Random Replacing of Known Values for Comparing Missing Data Imputation Models for Incomplete Air Quality Time Series

The data obtained from air quality monitoring stations, which are used to carry out studies using data mining techniques, present the problem of missing values. This paper describes a research work on missing data imputation. Among the most common methods, the method that best imputes values to the...

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Bibliographic Details
Main Authors: Álvarez de Prado, L. (Author), Bernardo Sánchez, A. (Author), Fernández López, D. (Author), Menéndez Fernández, M. (Author), Menéndez García, L.A (Author), Ortiz Marqués, A. (Author), Sokoła-Szewioła, V. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
air pollution
air quality
imputation
Kalman smoothing
k-nearest neighbours
linear imputation
predictive mean matching
random forest
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