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11th International Conference on Computer and Knowledge Engineering
A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series
Authors :
Ahmad Taheri
1
Shahriar Ghashghaei
2
Amin Beheshti
3
Keyvan RahimiZadeh
4
1- Yasouj University
2- Tehran University of Medical Sciences
3- Macquarie University
4- Yasouj University, Macquarie University
Keywords :
COVID-19, pandemic, machine learning, harmony search, metaheuristic algorithm
Abstract :
In this paper, a novel hybrid method called DMHS-GMDH is presented to predict the time series of COVID-19 outbreaks. In this way, a new version of Harmony Search (HS) algorithm, named Double Memory HS (DMHS), is designed to optimize the structure of a Group Method of Data Handling (GMDH) type neural network. We conduct a series of experiments by applying proposed method on real COVID-19 dataset to forecast new cases and deaths of COVID-19. The statistical analysis indicates that the DMHS-GMDH algorithm on average provides better results than other competitors and the results demonstrate how our approach at least improves coefficient of determination and RMSE by 21% and 45%, respectively.
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