Soft Computing-Based Generalized Least Deviation Method Algorithm for Modeling and Forecasting COVID-19 using Quasilinear Recurrence Equations

Abstract

 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.