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A novel sequential ensemble approach and particle swarm optimisation algorithm for forecasting: applied for COVID-19 cases as case study

Nader A. B. Al Theeb et al.

International Journal of Logistics Systems and Management2026https://doi.org/10.1504/ijlsm.2026.151734article
AJG 1
Weight
0.50

What the paper says

COVID-19 virus has spread to most countries around the world, negatively affecting people livelihood. Providing accurate forecasts of COVID-19 cases can help governments to find the optimal combination of measures. In this study, a sequential ensemble forecasting approach that combine the gated recurrent unit (GRU) model with particle swarm optimisation (PSO) algorithm is proposed for forecasting of COVID-19 cases. The PSO method was used to select the best hyperparameters of the base predictor of the proposed model. The t-test was used to statistically compare the suggested model against a single optimised GRU, in addition to other benchmark models. Results revealed the superiority of the proposed method. Further, adding models sequentially improved the forecasting quality, compared to a single PSO-GRU model, the mean error was reduced by 15.52%, 16.05%, 16.53%, 16.39%, and 12.83% in terms of RMSE, MAP, MAPE, RMSPE, and RMSLE, respectively.

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https://doi.org/https://doi.org/10.1504/ijlsm.2026.151734

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@article{nader2026,
  title        = {{A novel sequential ensemble approach and particle swarm optimisation algorithm for forecasting: applied for COVID-19 cases as case study}},
  author       = {Nader A. B. Al Theeb et al.},
  journal      = {International Journal of Logistics Systems and Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijlsm.2026.151734},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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