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A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics

Author

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  • Emerson Vilar de Oliveira

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Av. Salgado Filho, 3000, Campus Universitário, Lagoa Nova, Natal 59078-970, RN, Brazil)

  • Dunfrey Pires Aragão

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Av. Salgado Filho, 3000, Campus Universitário, Lagoa Nova, Natal 59078-970, RN, Brazil)

  • Luiz Marcos Garcia Gonçalves

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Av. Salgado Filho, 3000, Campus Universitário, Lagoa Nova, Natal 59078-970, RN, Brazil)

Abstract

The SARS-CoV-2 global pandemic prompted governments, institutions, and researchers to investigate its impact, developing strategies based on general indicators to make the most precise predictions possible. Approaches based on epidemiological models were used but the outcomes demonstrated forecasting with uncertainty due to insufficient or missing data. Besides the lack of data, machine-learning models including random forest, support vector regression, LSTM, Auto-encoders, and traditional time-series models such as Prophet and ARIMA were employed in the task, achieving remarkable results with limited effectiveness. Some of these methodologies have precision constraints in dealing with multi-variable inputs, which are important for problems like pandemics that require short and long-term forecasting. Given the under-supply in this scenario, we propose a novel approach for time-series prediction based on stacking auto-encoder structures using three variations of the same model for the training step and weight adjustment to evaluate its forecasting performance. We conducted comparison experiments with previously published data on COVID-19 cases, deaths, temperature, humidity, and air quality index (AQI) in São Paulo City, Brazil. Additionally, we used the percentage of COVID-19 cases from the top ten affected countries worldwide until May 4th, 2020. The results show 80.7 % and 10.3 % decrease in RMSE to entire and test data over the distribution of 50 trial-trained models, respectively, compared to the first experiment comparison. Also, model type#3 achieved 4th better overall ranking performance, overcoming the NBEATS, Prophet, and Glounts time-series models in the second experiment comparison. This model shows promising forecast capacity and versatility across different input dataset lengths, making it a prominent forecasting model for time-series tasks.

Suggested Citation

  • Emerson Vilar de Oliveira & Dunfrey Pires Aragão & Luiz Marcos Garcia Gonçalves, 2024. "A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics," IJERPH, MDPI, vol. 21(4), pages 1-19, April.
  • Handle: RePEc:gam:jijerp:v:21:y:2024:i:4:p:497-:d:1378041
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    References listed on IDEAS

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    1. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    2. Igor Gadelha Pereira & Joris Michel Guerin & Andouglas Gonçalves Silva Júnior & Gabriel Santos Garcia & Prisco Piscitelli & Alessandro Miani & Cosimo Distante & Luiz Marcos Garcia Gonçalves, 2020. "Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach," IJERPH, MDPI, vol. 17(14), pages 1-26, July.
    3. Shastri, Sourabh & Singh, Kuljeet & Kumar, Sachin & Kour, Paramjit & Mansotra, Vibhakar, 2020. "Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    5. Dunfrey Pires Aragão & Davi Henrique dos Santos & Adriano Mondini & Luiz Marcos Garcia Gonçalves, 2021. "National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil," IJERPH, MDPI, vol. 18(21), pages 1-24, November.
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    1. Aleksandr N. Grekov & Elena V. Vyshkvarkova & Aleksandr S. Mavrin, 2024. "Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms," Forecasting, MDPI, vol. 6(2), pages 1-14, May.

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