Research Article
Year 2023,
Issue: 1, 19 - 33, 15.08.2023
Abstract
Keywords
References
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- Görener A., Görener Ö. (2008). The contributions of automotive industry in Turkish economy and sectoral-expectances about the future. Journal Of Yaşar University, 7(26), 306-319. google scholar
- Hulsman, M., Borscheid, D., Friedrich, C.M., Reith, D. (2012). General sales forecast models for automobile markets and their analysis. Transactions On Machine Learning and Data Mining, 5(2), 65-86. google scholar
- Karaatlı, M., Helvacıoğlu, Ö., Ömürbek, N., Tokgöz, G. (2012). An artificial neural network based automobile sales forecasting . International Journal of Management Economics and Business, 8(17), 87-100. google scholar
- Kaya, K.S., Yildirim, Ö. (2020). A prediction model for automobile sales in Turkey using deep neural networks. Journal Of Industrial Engineering, 31(1), 57-74. google scholar
- Kuvvetli, Y., Dağsuyu, C., Oturakçı, M. (2015). A prediction approach based on artificial neural networks with consideration of environmental and economic indicators for car sales in Turkey. Journal of Industrial Engineering, 26(3), 23-31. google scholar
- Lin, Z.C., Wu, W.J. (1999). Multiple linearregression analysis of the overlay accuracy model zones. IEEE Trans. On Semiconductor Manufacturing, 12(2), 229 - 237. google scholar
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- Shahabuddin S. (2009). Forecasting automobile sales, Management Research News, 32(7), 670-682. google scholar
- Sharma, R., Sinha, A.K. (2012). Sales forecast of an automobile industry. International Journal of Computer Applications, 53(12), 25-28. google scholar
- Topal, İ. (2019). Çevrimiçi tüketici bütünleşmesi ve arama motoru verileri kullanılarak yapay sinir ağları ile otomobil satış tahmini, Nevşehir Hacı Bektaş Veli Üniversity Journal ofISS, 9(2), 534-551. google scholar
Year 2023,
Issue: 1, 19 - 33, 15.08.2023
Abstract
The automobile sector is the locomotive of industrialized countries. The employment opportunities it creates are of great value because of its interconnectedness with other industries and the value it adds. Demand forecasting studies in such an important sector are one of the main drivers for the provision of raw materials and services needed in the future. In this study, 10 independent variables are used that directly or indirectly affect the level of car sales, which is our dependent variable. These variables are gross domestic product, real sector confidence index, capital expenditures, household consumption expenditures, inflation rate, consumer confidence index, percentage of one-year term deposits, and oil barrel, gold, and dollar prices. The dataset used consists of annual data between 2000 and 2021. To examine the sales forecast model, two variables that affect minimum sales are first extracted from the model using the least squares method. Linear Regression, Decision Tree, Random Forest, Ridge, AdaBoost, Elastic-net, and Lasso Regression algorithms are applied to build a predictive model with these variables. The Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2) are used to compare the performance of the predictive models. This study proposes an approach for sectors affected directly or indirectly by automotive sales to gain foresight on this issue.
Keywords
References
- Alper, C. E., Mumcu, A. (2000). Türkiye’de otomobil talebinin tahmini. Research Report, Boğaziçi University. http:// ideas.econ.boun.edu.tr/content/wp/ISS_EC_05_01.pdf. google scholar
- Bulut, H. (2018). R uygulamaları ile çok değişkenli istatistiksel yöntemler. Nobel Academy. google scholar
- Civelek, Ç. (2021). Tractor Sales Forcasting for Turkey Using Artificial Neural Network. European Journal of Science and Technology ,31 (1), 375-381. google scholar
- Cutler, A., Cutler, D. R. And Stevens, J. R. (2012), Random forests, Ensemble Machine Learning Methods and Applications,45(1), 157-176. google scholar
- Devhunter. (2022, May 05), Rastgele Orman (Random Forest) Algoritması, Https://Devhunteryz.Wordpress. Com/2018/09/20/Rastgele-Ormanrandom-Forest- Algoritmasi/Comment-Page-1/ google scholar
- Dikmen, I. (2006). Otomotiv Sektörü Ve Rekabet Değerlendirme. Access Date: 10.12.2011, Http://Www.Kalder.Org.Tr/Genel/15kongre/Sunumlar/Isik_Dikmen.Doc. google scholar
- Euronews. (2022, June 04). Türkiye’de 100 kişiye düşen otomobil sayısı 14, AB’de ise 51, Https://Tr.Euronews. Com/2019/12/30/Turkiye-De-100-Kisiye-Dusen-Arac-Say-S-28-Ab- De-Ise-51. google scholar
- Freund, Y., Schapire, R.E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139. google scholar
- Görener A., Görener Ö. (2008). The contributions of automotive industry in Turkish economy and sectoral-expectances about the future. Journal Of Yaşar University, 7(26), 306-319. google scholar
- Hulsman, M., Borscheid, D., Friedrich, C.M., Reith, D. (2012). General sales forecast models for automobile markets and their analysis. Transactions On Machine Learning and Data Mining, 5(2), 65-86. google scholar
- Karaatlı, M., Helvacıoğlu, Ö., Ömürbek, N., Tokgöz, G. (2012). An artificial neural network based automobile sales forecasting . International Journal of Management Economics and Business, 8(17), 87-100. google scholar
- Kaya, K.S., Yildirim, Ö. (2020). A prediction model for automobile sales in Turkey using deep neural networks. Journal Of Industrial Engineering, 31(1), 57-74. google scholar
- Kuvvetli, Y., Dağsuyu, C., Oturakçı, M. (2015). A prediction approach based on artificial neural networks with consideration of environmental and economic indicators for car sales in Turkey. Journal of Industrial Engineering, 26(3), 23-31. google scholar
- Lin, Z.C., Wu, W.J. (1999). Multiple linearregression analysis of the overlay accuracy model zones. IEEE Trans. On Semiconductor Manufacturing, 12(2), 229 - 237. google scholar
- Mmpvizyon 2023 Otomotiv Sektör Raporu,” Https://Tubitak.Gov.Tr/Tubitak_Content_Files/Vizyon2023/Mm/Ek3. Pdf, 22.05.2022. google scholar
- Shahabuddin S. (2009). Forecasting automobile sales, Management Research News, 32(7), 670-682. google scholar
- Sharma, R., Sinha, A.K. (2012). Sales forecast of an automobile industry. International Journal of Computer Applications, 53(12), 25-28. google scholar
- Topal, İ. (2019). Çevrimiçi tüketici bütünleşmesi ve arama motoru verileri kullanılarak yapay sinir ağları ile otomobil satış tahmini, Nevşehir Hacı Bektaş Veli Üniversity Journal ofISS, 9(2), 534-551. google scholar
There are 18 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Software Testing, Verification and Validation |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | August 15, 2023 |
| Published in Issue | Year 2023 Issue: 1 |
