MAKROEKONOMİK DEĞİŞKENLER KULLANARAK TÜKETİCİ FİYAT ENDEKSİNİN TAHMİN EDİLMESİ: MAKİNE ÖĞRENMESİ VE DERİN ÖĞRENME YAKLAŞIMLARININ KARŞILAŞTIRMALI BİR ANALİZİ

Abstract

Türkiye ekonomisi tarih boyunca birçok ekonomik zorlıkla karşılaşmıştır. Bu noktada enflasyonun doğru bir şekilde tahmin edilmesi politika yapıcıları, işletmeler, yatırımcılar ve tüketiciler açısından oldukça önemlidir. Bu çalışmanın amacı Türkiye Tüketici Fiyat Endeksi’nin tahmin edilmesidir. Türkiye için TÜFE’nin tahmin edilmesinde üretici fiyat endeksi, M1 para arzı, altın fiyatı, dolar fiyatı, doğalgaz fiyatı ve faiz oranı değişkenleri kullanılmıştır. Araştırmada kullanılan değişkenler Merkez Bankasının Elektronik Veri Yönetim Sistemi olan EVDS üzerinden elde edilmiştir. Çalışmada Ocak 2003’ten Ağustos 2023’e kadar olan aylık veriler kullanılmıştır. Elde edilen veriler DDPG, XGBoost, SVR, KNN ve CNN-BiLSTM yöntemleri kullanılarak tahmin edilmiştir. Model performansları RMSE, MSE, MAE, MAPE ve R2 istatistik katsayıları kullanılarak karşılaştırılmıştır. Model performansları değerlendirildiğinde Türkiye için en iyi TÜFE tahmini SVR yöntemi tarafından elde edilmiştir.

Keywords

• TÜFE Tahmini
• SVR
• Zaman Serisi
• Karar Destek Sistemleri
• Makine Öğrenmesi
• Derin Öğrenme

FORECASTING CONSUMER PRICE INDEX USING MACROECONOMIC VARIABLES: A COMPARATIVE ANALYSIS OF MACHINE LEARNING AND DEEP LEARNING APPROACHES

Abstract

The Turkish economy has faced many economic difficulties throughout it's history. At this point, predicting inflation accurately is very important for policy makers, businesses, investors and consumers. This study aims to estimate the Turkish Consumer Price Index. Producer price index, M1 money supply, gold price, dollar price, natural gas price and interest rate variables were used to estimate the CPI for Turkey. The variables used in the research were obtained through EVDS, the Central Bank's Electronic Data Management System. Monthly data from January 2003 to August 2023 was used in the study. The obtained data were estimated using DDPG, XGBoost, SVR, KNN and CNN-BiLSTM methods. Model performances were compared using RMSE, MSE, MAE, MAPE and R2 statistical coefficients. When model performances were evaluated, the best CPI prediction for Turkey was obtained by the SVR method.

Keywords

• CIP Prediction
• SVR
• Time Series
• Decision Support System
• Machine Learning
• Deep Learning

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