This talk explores the application of the logistic map model for predicting historical data. Given the model's foundational role in population growth dynamics, its adaptation for data prediction offers novel insights into complex, nonlinear systems. We begin with an overview of the logistic map model, highlighting its chaotic characteristics and presenting its analytical solution. Subsequently, we formulate a least squares optimization problem to minimize the discrepancy between the model predictions and historical data. Utilizing the gradient descent method, we estimate the model parameters and iteratively refine the model solution to achieve optimal convergence. This process is summarized as a computational algorithm for practical implementation. To demonstrate the model's efficacy, we apply it to diverse datasets, including stock prices, sales volumes, and chemical reaction models. Our simulation results reveal accurate predictions, as evidenced by low mean square error values. These findings underscore the logistic map model's potential for satisfactory historical data prediction, paving the way for further exploration in various predictive analytics domains.

Sie Long Kek, PhD, CQRM, is currently a senior lecturer in the Department of Mathematics and Statistics, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia (Pagoh Campus). He received his M.Sc. and Ph.D. in mathematics from Universiti Teknologi Malaysia, Johor, Malaysia, in 2002 and 2011, respectively. He was a research associate at Curtin University of Technology in 2009 during his Ph.D. study. His research interests include optimization and control, operational research and management science, modelling and simulation, parameter estimation, Kalman filtering, and computational mathematics. He has published over 60 papers in refereed journals and six (6) book chapters. He reviews peer-reviewed research journals, including Automatica, Optimal Control, Applications and Methods, International Journal of Control, Heliyon, Journal of Industrial and Management Optimization, Measurement and Control, Hindawi Journal of Mathematics, and MDPI Journal of Risk and Financial Management. He has hosted three (3) research projects supported by the Ministry of Education Malaysia. He has supervised five (5) master's and three (3) Ph.D. students. Since 2015, he has been a certified quantitative risk management (CQRM) fellow. From 2021 to 2023, he was appointed head of the research focus group, Numerical Simulation and Applications (NSA).