İşsizlik Oranı Öngörülerinde Makine Öğrenimi Yaklaşımları: Türkiye Üzerine Bir Uygulama

Year 2024, Issue: 11, 1 - 14, 28.06.2024

Polad Aliyev

https://doi.org/10.58618/igdiriibf.1477486

Abstract

İşsizlik, sadece kapsamlı bir ekonomik sorun değil, aynı zamanda tüm ulusların odak noktası haline gelen karmaşık bir sosyal sorundur. İşsizlik sorununun doğru bir şekilde ele alınması, ülkenin kalkınmasıyla doğrudan ilişkilidir. Bu yönde oluşturulan politikaların başarası, işsizlik oranının doğru bir şekilde tahmin edilmesine dayanır. Bu makale, işsizlik oranı tahmininin yapılmasında yapay zekâ, makine öğrenimi ve klasik yöntemlerin kıyaslamasını amaçlamaktadır. Bu amaçla, Türkiye İstatistik Kurumu'ndan (TÜİK) Ocak 2005 verileriyle Aralık 2023 dönemini kapsayan işsizlik oranı verileri elde edilmiştir. Araştırmada ölçüt modeli olarak ARIMA, SARIMA modelleri, makine öğrenimi modelleri olarak Rassal Orman, XGBoost, LSTM ve GRU modelleri uygulanmıştır. Elde edilen sonuçlar, SARIMA'nın tahmin grafiğinin ve performans göstergelerinin ARIMA modeli performans değerlerinden daha iyi olduğunu göstermektedir. Makine öğrenimi modellerinde, MAPE dışındaki tüm hata ölçütleri SARIMA modelinin hata ölçütlerinden daha yüksektir. Ayrıca, bu modellerin belirleme katsayısı (R2) da SARIMA modelinin belirleme katsayısından (R2) daha büyüktür. Elde edilen sonuçlar en uygun metrik göstergeleri sergileyen makine öğrenimi yönteminin GRU modeli olduğunu ortaya koymuştur. Bu modelin MAE (Hataların Mutlak Değerlerinin Ortalaması) ve RMSE (Hata Kareler Ortalamasının Karekökü) değerleri en düşükken, R2 ise en yüksektir. Buna en yakın göstergeleri Rassal Orman modeli sergilemektedir.

Keywords

Yapay Zekâ, İşsizlik Oranı, GRU, LSTM, Tek Değişkenli Zaman Serisi Modelleri

Machine Learning Approaches in Unemployment Rate Prediction: An Application on Türkiye

Abstract

Unemployment is a complex economic and social issue affecting nations' development. Accurate unemployment rate estimation is crucial for successful policies aimed at addressing this issue. In this context, this article aims to compare artificial intelligence, machine learning, and classical methods of unemployment rate estimation. For this purpose, unemployment rate data covering the period between January 2005 and December 2023 were obtained from the Turkish Statistical Institute (TUİK). ARIMA and SARIMA models were applied as benchmark models, and Random Forest, XGBoost, LSTM, and GRU models were applied as machine learning models. The results show that the prediction graph and performance indicators of SARIMA are better than the ARIMA model performance values. In the machine learning models used in this study, all error measures except MAPE were higher than the error measures of the SARIMA model. Also, the coefficient of determination (R2) of these models was larger than that of the coefficient of determination of the SARIMA model. Furthermore, the results revealed that the machine learning method that exhibits the most favorable metric indicators is the GRU model. This model's MAE (Mean Absolute Value of Errors) and RMSE (Root Mean Square Error Squared) values were the lowest, while R2 was the highest. The Random Forest model exhibited the closest indicators.

Keywords

Artificial Intelligence, Unemployment Rate, GRU, LSTM, Univariate Time Series Models

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