Modelling Multi-Variable Business Forecast Trends Using Interpretive Ordinary Differential Equations

Abstract

Predicting business trends across current turbulent interdependent markets requires analytical models which extend beyond basic linear projections to handle complex evolving variable dependencies. The presented research develops an interpretation-based methodology for business trend modelling through ordinary differential equation systems with multiple components. This work adopts ODEs for qualitative evaluation of dynamic business connections between sales, inventory, marketing expense and economic variables since traditional prediction systems heavily depend on statistical and machine learning models. The goal stands in building understandable models with willingness to adapt that mirror actual business performance instead of building complex mathematical or computationally demanding models. This paper uses a mid-sized retail firm to illustrate how the proposed model successfully captures multiple business variable connections through effective projection of their real-world interactions. The research demonstrates how interpretive differential modelling reveals predictive patterns while identifying critical transition points and eventual stabilization states that serves as the foundation for business strategic decisions.