Advisor(s)
Marius Nkashama
Committee Member(s)
Chengcui Zhang
Junfang Li
Marius Nkashama
Roger Sidje
Shangbing Ai
School
College of Arts and Sciences
Document Type
Dissertation
Department (new version)
Applied Mathematics
Date of Award
9-11-2025
Abstract
The primary goal of this thesis is to improve upon existing dynamical system models in economics. Existing models utilizing Lotka-Volterra dynamics display a tendency to suffer from unrealistic forecasts dominated by exponential growth and decay. Additionally, current inverse methods are only applicable in differential equation systems with linear coefficients. In this body of work, we propose novel methods for handling these challenges.
ProQuest ID
Recommended Citation
Knowles, Jonathon, "Calibrating A Holling-Tanner Model With Applications To S&P500 Forecasting" (2025). All ETDs from UAB. 7333.
https://digitalcommons.library.uab.edu/etd-collection/7333
