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Research Article

Auto imputation enabled deep Temporal Convolutional Network (TCN) model for pm2.5 forecasting

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  • @ARTICLE{10.4108/eetsis.5102,
        author={K. Krishna Rani Samal},
        title={Auto imputation enabled deep Temporal Convolutional Network (TCN) model for pm2.5 forecasting},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={12},
        number={1},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={7},
        keywords={Deep learning, Pollution, Imputation, Forecasting, PM2.5, TCN},
        doi={10.4108/eetsis.5102}
    }
    
  • K. Krishna Rani Samal
    Year: 2024
    Auto imputation enabled deep Temporal Convolutional Network (TCN) model for pm2.5 forecasting
    SIS
    EAI
    DOI: 10.4108/eetsis.5102
K. Krishna Rani Samal1,*
  • 1: Vellore Institute of Technology University
*Contact email: kkrani2009@gmail.com

Abstract

Data imputation of missing values is one of the critical issues for data engineering, such as air quality modeling. It is challenging to handle missing pollutant values because they are collected at irregular and different times. Accurate estimation of those missing values is critical for the air pollution prediction task. Effective forecasting is a significant part of air quality modeling for a robust early warning system. This study developed a neural network model, a Temporal Convolutional Network (TCN) with an imputation block (TCN-I), to simultaneously perform data imputation and forecasting tasks. As pollution sensor data suffer from different types of missing values whose causes are varied, TCN is attempted to impute those missing values in this study and perform prediction tasks in a single model. The results prove that the TCN-I model outperforms the baseline models.

Keywords
Deep learning, Pollution, Imputation, Forecasting, PM2.5, TCN
Received
2024-02-13
Accepted
2024-06-16
Published
2024-07-11
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.5102

Copyright © 2024 K. K. R. Samal et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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