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Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction

Author

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  • Wenhui Ke

    (Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National Engineering Research Centre of Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China)

  • Yimin Lu

    (Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National Engineering Research Centre of Geospatial Information Technology, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350116, China)

Abstract

Due to the non-linear and non-stationary nature of daily new 2019 coronavirus disease (COVID-19) case time series, existing prediction methods struggle to accurately forecast the number of daily new cases. To address this problem, a hybrid prediction framework is proposed in this study, which combines ensemble empirical mode decomposition (EEMD), fuzzy entropy (FE) reconstruction, and a CNN-LSTM-ATT hybrid network model. This new framework, named EEMD-FE-CNN-LSTM-ATT, is applied to predict the number of daily new COVID-19 cases. This study focuses on the daily new case dataset from the United States as the research subject to validate the feasibility of the proposed prediction framework. The results show that EEMD-FE-CNN-LSTM-ATT outperforms other baseline models in all evaluation metrics, demonstrating its efficacy in handling the non-linear and non-stationary epidemic time series. Furthermore, the generalizability of the proposed hybrid framework is validated on datasets from France and Russia. The proposed hybrid framework offers a new approach for predicting the COVID-19 pandemic, providing important technical support for future infectious disease forecasting.

Suggested Citation

  • Wenhui Ke & Yimin Lu, 2024. "Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction," Mathematics, MDPI, vol. 12(3), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:493-:d:1333183
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    References listed on IDEAS

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    1. Peng, Yaohao & Nagata, Mateus Hiro, 2020. "An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
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