First-order linear ordinary differential equation for regression modelling

Sie, Long Kek and Chuei, Yee Chen and Sze, Qi Chan (2024) First-order linear ordinary differential equation for regression modelling. In: Electronics, Communications and Networks A.J. Tallón-Ballesteros et al. (Eds.).

Text P16588_f1c1ba8ad6226f1e7b00e8b320332fa2.pdf Download (427kB)

Abstract

This paper discusses the data-driven regression modelling using firstorder linear ordinary differential equation (ODE). First, we consider a set of actual data and calculate the numerical derivative. Then, a general equation for the firstorder linear ODE is introduced. There are two parameters, namely the regression parameters, in the equation, and their value will be determined in regression modelling. After this, a loss function is defined as the sum of squared errors to minimize the differences between estimated and actual data. A set of necessary conditions is derived, and the regression parameters are analytically determined. Based on these optimal parameter estimates, the solution of the first-order linear ODE, which matches the actual data trend, shall be obtained. Finally, two financial examples, the sales volume of Proton cars and the housing index, are illustrated. Simulation results show that an appropriate first-order ODE model for these examples can be suggested. From our study, the practicality of using the first-order linear ODE for regression modelling is significantly demonstrate

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	first-order linear ODE, regression modelling, loss function, parameter estimates, numerical solution
Subjects:	T Technology > T Technology (General)
Depositing User:	Mrs. Sabarina Che Mat
Date Deposited:	12 May 2024 03:25
Last Modified:	12 May 2024 03:25
URI:	http://eprints.uthm.edu.my/id/eprint/10906

Actions (login required)

View Item