First-order linear ordinary differential equation for regression modelling

Sie, Long Kek and Chuei, Yee Chen and Sze, Qi Chan (2023) First-order linear ordinary differential equation for regression modelling. In: THE 5TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND INTELLIGENT SYSTEMS (MLIS 2023).

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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 demonstrated

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:	03 Sep 2024 08:50
Last Modified:	03 Sep 2024 08:50
URI:	http://eprints.uthm.edu.my/id/eprint/11572

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