Proceedings of the 9th International Conference on Accounting, Management, and Economics 2024 (ICAME 2024)

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Fourier Series-Based Nonparametric Biresponse Regression for Climate Data Analysis

Authors
Hartina Husain1, *, Kusnaeni Kusnaeni1, Wahyuni Eka Sasmita1, Muhammad Rifki Nisardi1, Nur Rahmi1
1Bacharuddin Jusuf Habibie Institute of Technology, Parepare, South Sulawesi, Indonesia
*Corresponding author. Email: hartinahusain@ith.ac.id
Corresponding Author
Hartina Husain
Available Online 30 July 2025.
DOI
10.2991/978-94-6463-758-8_187How to use a DOI?
Keywords
Climate Change; Fourier Series; GCV; Regression
Abstract

The biresponse Fourier series nonparametric regression is a model designed to analyze the relationship between two correlated response variables and multiple predictor variables, The model employs Fourier series to effectively capture periodic or cyclic patterns within the data, making itu particularly suited for climate-related applications. This study aims to estimate the parameters of a mixed semiparametric regression model applied to climate data, utilizing the Weighted Least Squares (WLS) method for estimation. The analysis was conducted on climate data from South Sulawesi and West Sulawesi, focusing on sunshine duration and wind speed as the two response variables. The results demonstrate that the optimal model includes one oscillation, achieving a minimum Generalized Cross Validation (GCV) value of 0.737 and a high Coefficient of Determination (R2) of 99.49%, indicating an excellent fit of the model to the data. These findings suggest that the biresponse Fourier series model is a powerful tool for climate data analysis, offering valuable insights into the cyclic nature of weather patterns in regions like South Sulawesi and West Sulawesi, where periodic variations in climate phenomena can be observed.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 9th International Conference on Accounting, Management, and Economics 2024 (ICAME 2024)
Series
Advances in Economics, Business and Management Research
Publication Date
30 July 2025
ISBN
978-94-6463-758-8
ISSN
2352-5428
DOI
10.2991/978-94-6463-758-8_187How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Hartina Husain
AU  - Kusnaeni Kusnaeni
AU  - Wahyuni Eka Sasmita
AU  - Muhammad Rifki Nisardi
AU  - Nur Rahmi
PY  - 2025
DA  - 2025/07/30
TI  - Fourier Series-Based Nonparametric Biresponse Regression for Climate Data Analysis
BT  - Proceedings of the 9th International Conference on Accounting, Management, and Economics 2024 (ICAME 2024)
PB  - Atlantis Press
SP  - 2346
EP  - 2358
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-758-8_187
DO  - 10.2991/978-94-6463-758-8_187
ID  - Husain2025
ER  -