First-order linear ordinary differential equation for regression modelling

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, n...

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主要な著者: Sie, Long Kek, Chuei, Yee Chen, Sze, Qi Chan
フォーマット: Conference or Workshop Item
言語:English
出版事項: 2023
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オンライン・アクセス:http://eprints.uthm.edu.my/11572/1/P16588_f1c1ba8ad6226f1e7b00e8b320332fa2%204.pdf
http://eprints.uthm.edu.my/11572/
http://10.3233/FAIA231184
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spelling my.uthm.eprints.115722024-09-03T08:50:12Z http://eprints.uthm.edu.my/11572/ First-order linear ordinary differential equation for regression modelling Sie, Long Kek Chuei, Yee Chen Sze, Qi Chan T Technology (General) 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 2023-11-07 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/11572/1/P16588_f1c1ba8ad6226f1e7b00e8b320332fa2%204.pdf 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). http://10.3233/FAIA231184
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Sie, Long Kek
Chuei, Yee Chen
Sze, Qi Chan
First-order linear ordinary differential equation for regression modelling
description 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
format Conference or Workshop Item
author Sie, Long Kek
Chuei, Yee Chen
Sze, Qi Chan
author_facet Sie, Long Kek
Chuei, Yee Chen
Sze, Qi Chan
author_sort Sie, Long Kek
title First-order linear ordinary differential equation for regression modelling
title_short First-order linear ordinary differential equation for regression modelling
title_full First-order linear ordinary differential equation for regression modelling
title_fullStr First-order linear ordinary differential equation for regression modelling
title_full_unstemmed First-order linear ordinary differential equation for regression modelling
title_sort first-order linear ordinary differential equation for regression modelling
publishDate 2023
url http://eprints.uthm.edu.my/11572/1/P16588_f1c1ba8ad6226f1e7b00e8b320332fa2%204.pdf
http://eprints.uthm.edu.my/11572/
http://10.3233/FAIA231184
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