A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series

Ahamd Taheri, Shahriar Ghashghaei, Amin Beheshti, Keyvan RahimiZadeh

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationICCKE 2021
Subtitle of host publication11th International Conference on Computer Engineering and Knowledge
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages322-327
Number of pages6
ISBN (Electronic)9781665402088
DOIs
Publication statusPublished - 2021
Event11th International Conference on Computer Engineering and Knowledge, ICCKE 2021 - Mashhad, Iran, Islamic Republic of
Duration: 28 Oct 202129 Oct 2021

Conference

Conference11th International Conference on Computer Engineering and Knowledge, ICCKE 2021
Country/TerritoryIran, Islamic Republic of
CityMashhad
Period28/10/2129/10/21

Keywords

  • COVID-19
  • harmony search
  • machine learning
  • metaheuristic algorithm
  • pandemic

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