Amaç- Bu çalışmanın amacı, Makine Öğrenmesi yöntemlerinden yararlanarak geliştirilen modellerin zaman serilerinin öngörüsünde alternatif bir yöntem olup olmadığının incelenmesidir. Yöntem- Geleneksel olarak, Otoregresif Entegre Hareketli Ortalama (ARIMA) modeli, zaman serisi tahmininde en yaygın kullanılan doğrusal modellerden biridir. Çalışmada,ARIMA modellerinin yanı sıra Rassal Orman ve Hibrit Rassal Orman yöntemleri kullanılmış ve Türkiye Konut Fiyat Endeksi serisi için bu modellerin öngörü performansları karşılaştırılmıştır. Bulgular- Hibrit modelin konut fiyat endeksini öngörmede diğer yöntemlerden daha başarılı olduğu tespit edilmiştir. Sonuç- Sonuç olarak, ARIMA ve Makine Öğrenmesi yöntemini birleştiren hibrit modellerin, ekonomik ve finansal verilerin öngörüsünde alternatif bir yöntem olarak kullanılabileceği tespit edilmiştir.
The aim of this study is to explore whether the models developed using the machine learning methods are an alternative method in the prediction of time series. Method- Traditionally, the Otoregressive Integrated Moving Medium (ARIMA) model is one of the linear models commonly used in time series forecasts. In the study, the ARIMA models, as well as the Rassal Forest and Hybrid Rassal Forest methods were used and the forecast performance of these models for the Turkish Housing Price Index series was compared. The findings- Hybrid model has been found to be more successful in predicting the housing price index than other methods. As a result, hybrid models that combine the ARIMA and Machine Learning method have been found to be used as an alternative method in the prediction of economic and financial data.
Purpose- The aim of this study is to investigate whether the models developed by using Machine Learning methods are an alternative method for forecasting time series. Methodology-Traditionally, the Autoregressive Integrated Moving Average (ARIMA) model has been one of the most widely used linear models in time series forecasting. In the study, we use Random Forest and Hybrid Random Forest-ARIMA models besides the ARIMA model and compare their forecasting performance for the Turkish Housing Price Index series. Findings- The hybrid model was found to be more successful than other methods in forecasting the housing price index. Conclusion- As a result, hybrid models that combine ARIMA and machine learning method can be used an alternative method in forecasting economic and financial data
Alan : Sosyal, Beşeri ve İdari Bilimler
Dergi Türü : Ulusal
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