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Prediction of COVID-19 Data Using an ARIMA-LSTM Hybrid Forecast Model

Author

Listed:
  • Yongchao Jin

    (College of Sciences, North China University of Science and Technology, Tangshan 063210, China)

  • Renfang Wang

    (College of Sciences, North China University of Science and Technology, Tangshan 063210, China)

  • Xiaodie Zhuang

    (College of Sciences, North China University of Science and Technology, Tangshan 063210, China)

  • Kenan Wang

    (College of Sciences, North China University of Science and Technology, Tangshan 063210, China)

  • Honglian Wang

    (College of Sciences, North China University of Science and Technology, Tangshan 063210, China)

  • Chenxi Wang

    (College of Sciences, North China University of Science and Technology, Tangshan 063210, China)

  • Xiyin Wang

    (College of Sciences, North China University of Science and Technology, Tangshan 063210, China)

Abstract

The purpose of this study is to study the spread of COVID-19, establish a predictive model, and provide guidance for its prevention and control. Considering the high complexity of epidemic data, we adopted an ARIMA-LSTM combined model to describe and predict future transmission. A new method of the ARIMA-LSTM model paralleling by weight of regression coefficient was proposed. Then, we used the ARIMA-LSTM model paralleling by weight of regression coefficient, ARIMA model, and ARIMA-LSTM series model to predict the epidemic data in China, and we found that the ARIMA-LSTM model paralleling by weight of regression coefficient had the best prediction accuracy. In the ARIMA-LSTM model paralleling by weight of regression coefficient, MSE = 4049.913, RMSE = 63.639, MAPE = 0.205, R 2 = 0.837, MAE = 44.320. In order to verify the effectiveness of the ARIMA-LSTM model paralleling by weight of regression coefficient, we compared the ARIMA-LSTM model paralleling by weight of regression coefficient with the SVR model and found that ARIMA-LSTM model paralleling by weight of regression coefficient has better prediction accuracy. It was further verified with the epidemic data of India and found that the prediction accuracy of the ARIMA-LSTM model paralleling by weight of regression coefficient was still higher than that of the SVR model. In the ARIMA-LSTM model paralleling by weight of regression coefficient, MSE = 744,904.6, RMSE = 863.079, MAPE = 0.107, R 2 = 0.983, MAE = 580.348. Finally, we used the ARIMA-LSTM model paralleling by weight of regression coefficient to predict the future epidemic situation in China. We found that in the next 60 days, the epidemic situation in China will become a steady downward trend.

Suggested Citation

  • Yongchao Jin & Renfang Wang & Xiaodie Zhuang & Kenan Wang & Honglian Wang & Chenxi Wang & Xiyin Wang, 2022. "Prediction of COVID-19 Data Using an ARIMA-LSTM Hybrid Forecast Model," Mathematics, MDPI, vol. 10(21), pages 1-13, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:4001-:d:956239
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    References listed on IDEAS

    as
    1. Singh, Sarbjit & Parmar, Kulwinder Singh & Makkhan, Sidhu Jitendra Singh & Kaur, Jatinder & Peshoria, Shruti & Kumar, Jatinder, 2020. "Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
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