Comparison of decision tree algorithms in predicting consumer confidence index

Öz

The economic conditions and future expectations of individuals in a country can influence their spending and/or saving behaviors. The reflections of these behavioral patterns on the economy can be measured through the consumer confidence index. The aim of this study is to determine the most suitable algorithm for predicting the consumer confidence index by comparing various decision tree algorithms. Independent variables such as unemployment rate, BIST100 index, housing price index, exchange rate, and consumer price index, which are thought to impact the consumer confidence index, were used in the study. In the analyses, monthly data for the period of 01.2014-08.2024 were used, and 70% of the data was separated for training and 30% for testing. Decision tree-based algorithms, including Random Forest, XGBoost, LightGBM, and CatBoost, were applied to these data to develop predictive models. MSE, RMSE, MAE and MAPE error criteria were used to evaluate the performance of the algorithms. The results reveal that the Random Forest algorithm demonstrates the best performance in predicting the consumer confidence index.

Anahtar Kelimeler

• Consumer Confidence Index
• CatBoost
• LightGBM
• Random Forest
• XGBoost

Tüketici güven endeksi tahmininde karar ağacı algoritmalarının karşılaştırılması

Öz

Bir ülkedeki bireylerin ekonomik koşullara ve gelecekteki beklentilerine ilişkin algıları, harcama ve/veya birikim davranışlarını etkileyebilir. Bu davranışsal kalıpların ekonomiye yansıması, tüketici güven endeksi (TGE) aracılığıyla ölçülebilir. Bu çalışmanın amacı, çeşitli karar ağacı algoritmalarını karşılaştırarak tüketici güven endeksini tahmin etmek için en uygun algoritmayı belirlemektir. Çalışmada, tüketici güven endeksini etkileyebileceği düşünülen işsizlik oranı, BIST100 endeksi, konut fiyat endeksi, döviz kuru ve tüketici fiyat endeksi bağımsız değişkenleri kullanılmıştır. Analizlerde, 01.2014-08.2024 dönemi arasındaki aylık veriler kullanılmış ve verilerin %70’i eğitim için, %30’u ise test için ayrılmıştır. Karar ağacı tabanlı Random Forest, XGBoost, LightGBM ve CatBoost algoritmaları bu verilere uygulanarak tahmin modelleri geliştirilmiştir. Algoritmaların performansını değerlendirmek için MSE, RMSE, MAE ve MAPE hata kriterleri kullanılmıştır. Sonuçlar, Random Forest algoritmasının tüketici güven endeksini tahmin etmede en iyi performansı sergilediğini ortaya koymuştur.

Anahtar Kelimeler

• Tüketici Güven Endeksi
• CatBoost
• LightGBM
• Random Forest
• XGBoost

Kaynakça

1. Adusumilli, S., Bhatt, D., Wang, H., Bhattacharya, P., & Devabhaktuni, V. (2013). A low-cost INS/GPS integration methodology based on random forest regression. Expert Systems with Applications, 40(11), 4653-4659.
2. Akkuş, H. T., & Zeren, F. (2019). Tüketici güven endeksi ve Katılım-30 İslami hisse senedi endeksi arasındaki saklı ilişkinin araştırılması: Türkiye örneği. Third Sector Social Economic Review, 54(1), 53-70.
3. Beşiktaşlı, D. K., & Cihangir, Ç. K. (2020). Tüketici güven endeksinin finansal piyasalara ve makroekonomik yapıya etkisi. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 5(1), 54-67.
4. Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. Caleiro, A. B. (2021). Learning to classify the consumer confidence indicator (in Portugal). Economies, 9(3), 1-12.
5. Canöz, İ. (2018). Borsa İstanbul 100 endeksi ile tüketici güven endeksleri arasındaki nedensellik ilişkisi: Türkiye örneği. Fiscaoeconomia, 2(1), 136-153.
6. Çelik, S. (2010). An unconventional analysis of consumer confidence index for the Turkish economy. International Journal of Economics and Finance Studies, 2(1), 121-129.
7. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
8. Durgun, A. (2019). Türkiye’de tüketici ve reel kesim güven endeksi ile seçilmiş makro değişkenler arasındaki ilişkiler: 2010-2018. Journal of Management and Economics Research, 17(1), 314-332.

9. Ge, J., Zhao, L., Yu, Z., Liu, H., Zhang, L., Gong, X., & Sun, H. (2022). Prediction of greenhouse tomato crop evapotranspiration using XGBoost machine learning model. Plants, 11(15), 1-17.
10. Grzywińska-Rąpca, M., & Ptak-Chmielewska, A. (2023). Backward assessments or expectations: What determines the consumer confidence index more strongly? Panel model based on the CCI of European countries. Wiadomości Statystyczne. The Polish Statistician, 68(2), 1-15.
11. Han, H., Li, Z., & Li, Z. (2023). Using machine learning methods to predict consumer confidence from search engine data. Sustainability, 15(4), 1-12.
12. Huang, J. C., Tsai, Y. C., Wu, P. Y., Lien, Y. H., Chien, C. Y., Kuo, C. F., ... & Kuo, C. H. (2023). Predictive modeling of blood pressure during hemodialysis: A comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method. Computer Methods and Programs in Biomedicine, 195, 1-6.
13. Huang, S. (2023, December). Price Prediction and Analysis Based on the LightGBM Regression Model. In 2023 4th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+ AI) (pp. 580-584). IEEE.
14. Islam, T. U., & Mumtaz, M. N. (2016). Consumer confidence index and economic growth: An empirical analysis of EU countries. EuroEconomica, 35(2), 17-22.
15. Jimenez, B. S. (2013). Strategic planning and the fiscal performance of city governments during the Great Recession. The American Review of Public Administration, 43(5), 581-601.
16. Joo, C., Park, H., Lim, J., Cho, H., & Kim, J. (2023). Machine learning-based heat deflection temperature prediction and effect analysis in polypropylene composites using catboost and shapley additive explanations. Engineering Applications of Artificial Intelligence, 126, 1-12.
17. Karagöz, D., & Aktaş, S. (2015). Evaluation of consumer confidence index of Central Bank of Turkey consumer tendency survey. TOJSAT, 5(3), 31-36.
18. Kuhn, M., and K. Johnson. 2013. Applied Predictive Modeling.
19. Li, B., Chen, G., Si, Y., Zhou, X., Li, P., Li, P., & Fadiji, T. (2022). GNSS/INS integration based on machine learning LightGBM model for vehicle navigation. Applied Sciences, 12(11), 1-12.
20. Lin, Y. C., Sung, B., & Park, S. D. (2024). Integrated systematic framework for forecasting China’s consumer confidence: A machine learning approach. Systems, 12(11), 1-33.
21. Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S., & Sun, Y. (2021). Combination of feature selection and catboost for prediction: The first application to the estimation of aboveground biomass. Forests, 12(2), 1-21.
22. Mariadass, D. A., Moung, E. G., Sufian, M. M., & Farzamnia, A. (2022, November). EXtreme gradient boosting (XGBoost) regressor and shapley additive explanation for crop yield prediction in agriculture. In 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 219-224). IEEE.
23. Mazurek, J., & Mielcová, E. (2017). Is consumer confidence index a suitable predictor of future economic growth? An evidence from the USA. E & M Ekonomie A Management, 20(2), 30–45.
24. Mpofu, K., Adenuga, O. T., Popoola, O. M., & Mathebula, A. (2023). LightGBM and SVM algorithms for predicting synthetic load profiles using a non-intrusive approach. https://doi:10.20944/preprints202308.0257.v1.
25. Münyas, T. (2019). Borsa İstanbul Endeksleri ile Güven Endeksleri arasindaki ilişkinin araştirilmasi üzerine bir inceleme. TESAM Akademi Dergisi, 6, 299-320.
26. Nguyen, T. T., Nguyen, H. G., Lee, J. Y., Wang, Y. L., & Tsai, C. S. (2023). The consumer price index prediction using machine learning approaches: Evidence from the United States. Heliyon, 9(10), 1-17.
27. Ohmura, H. (2020). A new measurement for Japanese Consumer Confidence Index. Economics Bulletin, 40(2), 1557-1569.
28. Pesantez-Narvaez, J., Guillen, M., & Alcañiz, M. (2019). Predicting motor insurance claims using telematics data—XGBoost versus logistic regression. Risks, 7(2), 1-16.
29. Ponsam, J. G., Gracia, S. J. B., Geetha, G., Karpaselvi, S., & Nimala, K. (2021, December). Credit risk analysis using LightGBM and a comparative study of popular algorithms. In 2021 4th International Conference on Computing and Communications Technologies (ICCCT) (pp. 634-641). IEEE.
30. Pramanik, P., Jana, R. K., & Ghosh, I. (2024). AI readiness enablers in developed and developing economies: Findings from the XGBoost regression and explainable AI framework. Technological Forecasting and Social Change, 205, 1-18.
31. Qiu, Y. (2020). Forecasting the Consumer Confidence Index with tree-based MIDAS regressions. Economic Modelling, 91, 247-256.
32. Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. J. O. G. R. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804-818.
33. Şeyranlıoğlu, O. (2023). Tüketici Güven Endeksi ile finansal yatırım araçlarının reel getirileri arasındaki nedensellik ilişkilerinin değerlendirilmesi: Türkiye örneği. Karadeniz Sosyal Bilimler Dergisi, 15(29), 572-593.
34. Shayaa, S., Ainin, S., Jaafar, N. I., Zakaria, S. B., Phoong, S. W., Yeong, W. C., ... & Zahid Piprani, A. (2018). Linking consumer confidence index and social media sentiment analysis. Cogent Business & Management, 5(1), 1-12.
35. Su, C. W., Meng, X. L., Tao, R., & Umar, M. (2023). Chinese consumer confidence: A catalyst for the outbound tourism expenditure? Tourism Economics, 29(3), 696-717.
36. Tjandrasa, B. B., & Dewi, V. I. (2022). Determinants of Consumer Confidence Index to predict the economy in Indonesia. Australasian Accounting, Business and Finance Journal, 16(4), 3-13.
37. Vitkauskaitė, A. (2024). Evaluation of consumer confidence indicators using social media and administrative data (Doctoral dissertation, Vilniaus universitetas.).
38. Wang, P., Li, X., Zhan, X., Zhang, Y., Yan, Y., & Meng, W. (2019). Building consumer confidence index based on social media big data. Human Behavior and Emerging Technologies, 1(3), 261-268.
39. Yang, X., & Chen, Z. (2021, April). A hybrid short-term load forecasting model based on catboost and lstm. In 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) (pp. 328-332). IEEE.
40. Zhang, W., Wu, C., Li, Y., Wang, L., & Samui, P. (2021). Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15(1), 27-40.
41. Zhu, J., Su, Y., Liu, Z., Liu, B., Sun, Y., Gao, W., & Fu, Y. (2022). Real‐time biomechanical modelling of the liver using LightGBM model. The International Journal of Medical Robotics and Computer Assisted Surgery, 18(6), 1-11.