Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided gradient-based optimization

This research delves into the significance of influenza outbreaks in public health, particularly the importance of accurate forecasts using weekly Influenza-like illness (ILI) rates. The present work develops a novel hybrid machine-learning model by combining singular value decomposition with kernel...

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Veröffentlicht in:	Computers in biology and medicine 2024-02, Vol.169, p.107888, Article 107888
Hauptverfasser:	Hongliang, Guo, Zhiyao, Zhang, Ahmadianfar, Iman, Escorcia-Gutierrez, José, Aljehane, Nojood O., Li, Chengye
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Coronaviruses COVID-19 Crude oil prices Decision making Decomposition Disease Outbreaks Epidemics Fatalities Feature selection Forecasting Forecasting techniques GBO algorithm Generalized linear models Health care Humans Influenza Influenza forecasting Influenza, Human - epidemiology Information systems Kernel ridge regression Machine learning MARCOS method Mathematical models Medical research Multi-step ahead Multiple criterion Neural networks Neural Networks, Computer Optimization Optimization techniques Pandemics Public Health Regression Robustness (mathematics) Singular value decomposition Singular value decomposition (SVD) Time series
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