Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement
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DOI: 10.1016/j.chaos.2021.111789
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Cited by:
- Eunju Hwang, 2023. "Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models," Forecasting, MDPI, vol. 6(1), pages 1-18, December.
- Ghimire, Sujan & AL-Musaylh, Mohanad S. & Nguyen-Huy, Thong & Deo, Ravinesh C. & Acharya, Rajendra & Casillas-Pérez, David & Yaseen, Zaher Mundher & Salcedo-Sanz, Sancho, 2025. "Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts," Applied Energy, Elsevier, vol. 378(PA).
- He, Yaoyao & Wang, Yun & Wang, Shuo & Yao, Xin, 2022. "A cooperative ensemble method for multistep wind speed probabilistic forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
- Alexander Gusev & Alexander Chervyakov & Anna Alexeenko & Evgeny Nikulchev, 2023. "Particle Swarm Training of a Neural Network for the Lower Upper Bound Estimation of the Prediction Intervals of Time Series," Mathematics, MDPI, vol. 11(20), pages 1-12, October.
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Keywords
COVID-19; HAR model; Prediction interval; Mean interval score; Bootstrap procedure;All these keywords.
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