FORECASTING OF TURKISH HOUSING PRICE INDEX: ARIMA, RANDOM FOREST, ARIMA-RANDOM FOREST

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Atıf İçin Kopyala

Çağlayan Akay E., Topal K. H., Kızılarslan Ş., Bülbül H.

Istanbul Finance Congress, İstanbul, Türkiye, 01 Kasım 2019, ss.7-11, (Tam Metin Bildiri)

• Yayın Türü: Bildiri / Tam Metin Bildiri
• Doi Numarası: 10.17261/pressacademia.2019.1134
• Basıldığı Şehir: İstanbul
• Basıldığı Ülke: Türkiye
• Sayfa Sayıları: ss.7-11
• Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

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.

Keywords:ARIMA, random forest, ARIMA-random forest, hybrid, machine learning.

JEL Codes: C52, C58, C61