 This paper proposes a chaotic computing paradigm to identify the parameters of an autoregressive exogenous ARX model. Chaos theory is used to optimize the parameters of the ARX model using an improved chaotic gray wolf optimizer, ICGW, which is compared to other optimization algorithms such as genetic algorithms, GA, and particle swarm optimization, PSO. The results show that the ICGW outperforms both GA and PSO in terms of accuracy, robustness, and reliability. This article was authored by Kizr Mahmoud, Naveed Ishtiak Chaudhary, Zeshan Aslam Khan, and others.