Tüketici güven endeksi tahmininde karar ağacı algoritmalarının karşılaştırılması

Year 2025, Volume: 7 Issue: 12, 254 - 272, 01.04.2025

Özlem Akay , İlkay Altındağ

Abstract

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.

Keywords

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

Comparison of decision tree algorithms in predicting consumer confidence index

Abstract

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.

Keywords

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

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