Soft Computing-Based Generalized Least Deviation Method Algorithm for Modeling and Forecasting COVID-19 using Quasilinear Recurrence Equations
Autor: | Mostafa Abotaleb |
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Jazyk: | angličtina |
Rok vydání: | 2024 |
Předmět: | |
Zdroj: | Iraqi Journal for Computer Science and Mathematics, Vol 5, Iss 3 (2024) |
Druh dokumentu: | article |
ISSN: | 2958-0544 2788-7421 14057034 |
DOI: | 10.52866/ijcsm.2024.05.03.028 |
Popis: | This study introduces an advanced algorithm based on the Generalized Least Deviation Method (GLDM) tailored for the univariate time series analysis of COVID-19 data. At the core of this approach is the optimization of a loss function, strategically designed to enhance the accuracy of the model’s predictions. The algorithm leverages second-order terms, crucial for capturing the complexities inherent in time series data. Our findings reveal that by optimizing the loss function and effectively utilizing second-order model dynamics, there is a marked improvement in the predictive performance. This advancement leads to a robust and practical forecasting tool, significantly enhancing the accuracy and reliability of univariate time series forecasts in the context of monitoring COVID-19 trends. |
Databáze: | Directory of Open Access Journals |
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