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, nam...
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| Format: | Conference or Workshop Item |
| Language: | en |
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2024
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| Online Access: | http://eprints.uthm.edu.my/10906/1/P16588_f1c1ba8ad6226f1e7b00e8b320332fa2.pdf http://eprints.uthm.edu.my/10906/ http://10.3233/FAIA231184 |
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| _version_ | 1833419368993652736 |
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| 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 |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 demonstrate |
| format | Conference or Workshop Item |
| id | my.uthm.eprints-10906 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2024 |
| record_format | eprints |
| spelling | my.uthm.eprints-109062024-05-12T03:25:50Z http://eprints.uthm.edu.my/10906/ 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 demonstrate 2024 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/10906/1/P16588_f1c1ba8ad6226f1e7b00e8b320332fa2.pdf 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.). http://10.3233/FAIA231184 |
| spellingShingle | T Technology (General) Sie, Long Kek Chuei, Yee Chen Sze, Qi Chan First-order linear ordinary differential equation for regression modelling |
| title | 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_short | First-order linear ordinary differential equation for regression modelling |
| title_sort | first-order linear ordinary differential equation for regression modelling |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/10906/1/P16588_f1c1ba8ad6226f1e7b00e8b320332fa2.pdf http://eprints.uthm.edu.my/10906/ http://10.3233/FAIA231184 |
| url_provider | http://eprints.uthm.edu.my/ |