Hybrid Model Approaches for Accurate Time Series Predicting of COVID-19 Cases

Authors

• Hussein Alkattan, Department of System Programming, South Ural State University, Chelyabinsk, Russia.Follow
• Bashar Talib Al-Nuaimi, Computer Science Department, University of Diyala, Diyala 32001, Iraq
• Alhumaima Ali Subhi, Electronic Computer Centre, University of Diyala, Diyala, Iraq
• Benson Turyasingura, Faculty of Agriculture and Environmental Sciences, Kabale University, P. O. Box 317, Kabale, Uganda

Abstract

This study compares the performance of ARIMA, LSTM, and hybrid models in predicting COVID-19 cases and analyzing forecast errors. Utilizing real-world data, the models were assessed for accuracy and trend prediction over numerous months. Heatmaps of prediction errors revealed that the hybrid model reliably outperformed ARIMA and LSTM by accomplishing lower error extents. The findings highlight the preferences of combining statistical and deep learning approaches for time series predicting. The results contribute to progressing predicting accuracy, which is basic for pandemic response arranging and public health administration.

Recommended Citation

Alkattan, Hussein; Al-Nuaimi, Bashar Talib; Subhi, Alhumaima Ali; and Turyasingura, Benson (2024) "Hybrid Model Approaches for Accurate Time Series Predicting of COVID-19 Cases," Mesopotamian Journal of Artificial Intelligence in Healthcare: Vol. 2: Iss. 1, Article 17.
DOI: https://doi.org/10.58496/MJAIH/2024/017

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.