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Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

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  • Singh, Sarbjit
  • Parmar, Kulwinder Singh
  • Kumar, Jatinder
  • Makkhan, Sidhu Jitendra Singh

Abstract

Everywhere around the globe, the hot topic of discussion today is the ongoing and fast-spreading coronavirus disease (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Earlier detected in Wuhan, Hubei province, in China in December 2019, the deadly virus engulfed China and some neighboring countries, which claimed thousands of lives in February 2020. The proposed hybrid methodology involves the application of discreet wavelet decomposition to the dataset of deaths due to COVID-19, which splits the input data into component series and then applying an appropriate econometric model to each of the component series for making predictions of death cases in future. ARIMA models are well known econometric forecasting models capable of generating accurate forecasts when applied on wavelet decomposed time series. The input dataset consists of daily death cases from most affected five countries by COVID-19, which is given to the hybrid model for validation and to make one month ahead prediction of death cases. These predictions are compared with that obtained from an ARIMA model to estimate the performance of prediction. The predictions indicate a sharp rise in death cases despite various precautionary measures taken by governments of these countries.

Suggested Citation

  • Singh, Sarbjit & Parmar, Kulwinder Singh & Kumar, Jatinder & Makkhan, Sidhu Jitendra Singh, 2020. "Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    • Handle: RePEc:eee:chsofr:v:135:y:2020:i:c:s0960077920302666
    DOI: 10.1016/j.chaos.2020.109866
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    References listed on IDEAS

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    1. Guerrero, Victor M., 1991. "ARIMA forecasts with restrictions derived from a structural change," International Journal of Forecasting, Elsevier, vol. 7(3), pages 339-347, November.
    2. Yousefi, Shahriar & Weinreich, Ilona & Reinarz, Dominik, 2005. "Wavelet-based prediction of oil prices," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 265-275.
    3. Guy Melard & Jean-Michel Pasteels, 2000. "Automatic ARIMA modeling including interventions, using time series expert software," ULB Institutional Repository 2013/13744, ULB -- Universite Libre de Bruxelles.
    4. Ramsey, J.B., 2002. "Wavelets in Economics and Finance: Past and Future," Working Papers 02-02, C.V. Starr Center for Applied Economics, New York University.
    5. Melard, G. & Pasteels, J. -M., 2000. "Automatic ARIMA modeling including interventions, using time series expert software," International Journal of Forecasting, Elsevier, vol. 16(4), pages 497-508.
    6. Bianchi, Lisa & Jarrett, Jeffrey & Choudary Hanumara, R., 1998. "Improving forecasting for telemarketing centers by ARIMA modeling with intervention," International Journal of Forecasting, Elsevier, vol. 14(4), pages 497-504, December.
    7. Ramsey James B., 2002. "Wavelets in Economics and Finance: Past and Future," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-29, November.
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    Cited by:

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    6. Dinesh K. Sharma & H. S. Hota & Kate Brown & Richa Handa, 2022. "Integration of genetic algorithm with artificial neural network for stock market forecasting," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 828-841, June.
    7. Claudio Barbiellini Amidei & Ugo Fedeli & Nicola Gennaro & Laura Cestari & Elena Schievano & Manuel Zorzi & Paolo Girardi & Veronica Casotto, 2023. "Estimating Overall and Cause-Specific Excess Mortality during the COVID-19 Pandemic: Methodological Approaches Compared," IJERPH, MDPI, vol. 20(11), pages 1-13, May.
    8. 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).
    9. Batistela, Cristiane M. & Correa, Diego P.F. & Bueno, Átila M & Piqueira, José Roberto C., 2021. "SIRSi compartmental model for COVID-19 pandemic with immunity loss," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    10. Charu Arora & Poras Khetarpal & Saket Gupta & Nuzhat Fatema & Hasmat Malik & Asyraf Afthanorhan, 2023. "Mathematical Modelling to Predict the Effect of Vaccination on Delay and Rise of COVID-19 Cases Management," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    11. Duan, Huiming & Nie, Weige, 2022. "A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    12. Perone, G., 2020. "Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/18, HEDG, c/o Department of Economics, University of York.
    13. Nathan Zavanelli, 2023. "Wavelet Analysis for Time Series Financial Signals via Element Analysis," Papers 2301.13255, arXiv.org.
    14. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    15. Daren Zhao & Huiwu Zhang & Qing Cao & Zhiyi Wang & Sizhang He & Minghua Zhou & Ruihua Zhang, 2022. "The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-18, February.
    16. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    17. Aldila, Dipo, 2020. "Analyzing the impact of the media campaign and rapid testing for COVID-19 as an optimal control problem in East Java, Indonesia," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    18. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    19. Yeşilkanat, Cafer Mert, 2020. "Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

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