Multi-model transfer function approach tuned by PSO for predicting stock market implied volatility explained by uncertainty indexes.

This paper studies the forecasting power of uncertainty emanating from the commodities market, energy market, economic policy, and geopolitical threats to the CBOE Volatility Index (VIX). In this study, the relationship between the various uncertainty metrics throughout the period 2012-2022, using a multi-model transfer function technique optimized by particle swarm optimization (PSO) is estimated. Furthermore, we utilize PSO for parameter optimization within the multi-model framework, improving model performance and convergence speed.

Forecasting commodity prices: empirical evidence using deep learning tools.

Since the last two decades, financial markets have exhibited several transformations owing to recurring crises episodes that has led to the development of alternative assets. Particularly, the commodity market has attracted attention from investors and hedgers. However, the operational research stream has also developed substantially based on the growth of the artificial intelligence field, which includes machine learning and deep learning.

Analysis and modeling of COVID-19 epidemic dynamics in Saudi Arabia using SIR-PSO and machine learning approaches.

Introduction: COVID-19 has become a global concern because it has extensive damage to health, social and economic systems worldwide. Consequently, there is an urgent need to develop tools to understand, analyze, monitor and control further outbreaks of the disease.

Methodology: The Susceptible Infected Recovered-Particle SwarmOptimization model and the feed-forward artificial neural network model were separately developed to model COVID-19 dynamics based on daily time-series data reported by the Saudi authorities from March 2, 2020 to February 21, 2021.

Generalized Richards model for predicting COVID-19 dynamics in Saudi Arabia based on particle swarm optimization Algorithm.

COVID-19 pandemic is spreading around the world becoming thus a serious concern for health, economic and social systems worldwide. In such situation, predicting as accurately as possible the future dynamics of the virus is a challenging problem for scientists and decision-makers. In this paper, four phenomenological epidemic models as well as Suspected-Infected-Recovered (SIR) model are investigated for predicting the cumulative number of infected cases in Saudi Arabia in addition to the probable end-date of the outbreak.