PM2.5 Time Series Imputation with Moving Averages, Smoothing, and Linear Interpolation

PM2.5 Time Series Imputation with Moving Averages, Smoothing, and Linear Interpolation

In this work, a novel model for hourly PM2.5 time series imputation is proposed for the estimation of missing values in different gap sizes, including 1, 3, 6, 12, and 24 h. The proposed model is based on statistical techniques such as moving averages, linear interpolation smoothing, and linear inte...

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Bibliographic Details
Main Authors: Anibal Flores, Hugo Tito-Chura, Osmar Cuentas-Toledo, Victor Yana-Mamani, Deymor Centty-Villafuerte
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Computers
Subjects:
moving averages
smoothing
linear interpolation
PM2.5 time series
univariate imputation
Online Access:https://www.mdpi.com/2073-431X/13/12/312
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https://www.mdpi.com/2073-431X/13/12/312

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