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A Novel Hybrid Dmhs-Gmdh Algorithm to Predict Covid-19 Pandemic Time Series Publisher



Taheri A1 ; Ghashghaei S2 ; Beheshti A3 ; Rahimizadeh K1, 3
Authors

Source: ICCKE 2021 - 11th International Conference on Computer Engineering and Knowledge Published:2021


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. © 2021 IEEE.
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