COVID-19 pandemic forecasting using CNN-LSTM: a hybrid approach

DOI10.1155/2021/8785636zbMath1486.92302OpenAlexW3187628238MaRDI QIDQ832776

Zuhaira M. Zain, Nazik M. Alturki

Publication date: 25 March 2022

Published in: Journal of Control Science and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2021/8785636

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Epidemiology (92D30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Uses Software

• LSTM
• XGBoost
• ImageNet
• prophet
• AlexNet

Cites Work

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• Bagging predictors
• prophet
• Time series forecasting of COVID-19 transmission in Canada using LSTM networks
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
• Forecasting at Scale
• Stochastic gradient boosting.
• Using iterated bagging to debias regressions

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