Prediction of furniture exports in Türkiye using machine learning methods.
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| Title: | Prediction of furniture exports in Türkiye using machine learning methods. |
|---|---|
| Authors: | AKYÜZ, İlker1 iakyuz@ktu.edu.tr, ERSEN, Nadir2, BARDAK, Timuçin3, BARDAK, Selahattin4 |
| Source: | Turkish Journal of Agriculture & Forestry. 2026, Vol. 50 Issue 3, p342-356. 15p. |
| Subjects: | Machine learning, Random forest algorithms, Decision trees, Deep learning, Economic indicators, Furniture industry, Economic forecasting |
| Geographic Terms: | Türkiye |
| Abstract: | In this study, it was aimed to predict Türkiye's furniture exports for the period from January 2010 to May 2025 using machine learning methods based on macroeconomic indicators. The dataset consisted of 10 economic variables, including the furniture industry production index, capacity utilization rate, import volume, total exports, real effective exchange rate, producer price index, money supply, and oil prices, with 185 observations per variable and a total of 2035 data points. During the data preprocessing phase, no missing or outlier values were detected, and the data were divided into 70% training and 30% testing subsets. Decision tree, random forest, and deep learning models were developed using the software RapidMiner Studio, and hyperparameter optimization was performed through the grid search method. Model performances were evaluated using root mean square deviation, mean absolute error, mean absolute percentage error, and R2 metrics. The results indicated that all models predicted furniture exports with high accuracy. The best performance was achieved by the random forest model, with R² = 0.977 and MAPE = 5.28% in the testing phase. Furthermore, the variable importance analysis based on the random forest model revealed that the furniture industry production index and capacity utilization rate were the most significant determinants of exports, highlighting the production-driven nature of the sector. These findings demonstrate that machine learning methods can be effectively used in forecasting economic indicators and that Türkiye's furniture exports can be reliably predicted through data-driven approaches. [ABSTRACT FROM AUTHOR] |
| Copyright of Turkish Journal of Agriculture & Forestry is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prediction of furniture exports in Türkiye using machine learning methods. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22AKYÜZ%2C+İlker%22">AKYÜZ, İlker</searchLink><relatesTo>1</relatesTo><i> iakyuz@ktu.edu.tr</i><br /><searchLink fieldCode="AR" term="%22ERSEN%2C+Nadir%22">ERSEN, Nadir</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22BARDAK%2C+Timuçin%22">BARDAK, Timuçin</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22BARDAK%2C+Selahattin%22">BARDAK, Selahattin</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Turkish+Journal+of+Agriculture+%26+Forestry%22">Turkish Journal of Agriculture & Forestry</searchLink>. 2026, Vol. 50 Issue 3, p342-356. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Economic+indicators%22">Economic indicators</searchLink><br /><searchLink fieldCode="DE" term="%22Furniture+industry%22">Furniture industry</searchLink><br /><searchLink fieldCode="DE" term="%22Economic+forecasting%22">Economic forecasting</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Türkiye%22">Türkiye</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this study, it was aimed to predict Türkiye's furniture exports for the period from January 2010 to May 2025 using machine learning methods based on macroeconomic indicators. The dataset consisted of 10 economic variables, including the furniture industry production index, capacity utilization rate, import volume, total exports, real effective exchange rate, producer price index, money supply, and oil prices, with 185 observations per variable and a total of 2035 data points. During the data preprocessing phase, no missing or outlier values were detected, and the data were divided into 70% training and 30% testing subsets. Decision tree, random forest, and deep learning models were developed using the software RapidMiner Studio, and hyperparameter optimization was performed through the grid search method. Model performances were evaluated using root mean square deviation, mean absolute error, mean absolute percentage error, and R2 metrics. The results indicated that all models predicted furniture exports with high accuracy. The best performance was achieved by the random forest model, with R² = 0.977 and MAPE = 5.28% in the testing phase. Furthermore, the variable importance analysis based on the random forest model revealed that the furniture industry production index and capacity utilization rate were the most significant determinants of exports, highlighting the production-driven nature of the sector. These findings demonstrate that machine learning methods can be effectively used in forecasting economic indicators and that Türkiye's furniture exports can be reliably predicted through data-driven approaches. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Turkish Journal of Agriculture & Forestry is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.55730/1300-011X.3355 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 342 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Decision trees Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Economic indicators Type: general – SubjectFull: Furniture industry Type: general – SubjectFull: Economic forecasting Type: general – SubjectFull: Türkiye Type: general Titles: – TitleFull: Prediction of furniture exports in Türkiye using machine learning methods. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: AKYÜZ, İlker – PersonEntity: Name: NameFull: ERSEN, Nadir – PersonEntity: Name: NameFull: BARDAK, Timuçin – PersonEntity: Name: NameFull: BARDAK, Selahattin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1300011X Numbering: – Type: volume Value: 50 – Type: issue Value: 3 Titles: – TitleFull: Turkish Journal of Agriculture & Forestry Type: main |
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