Gold Price Forecasting Using LSTM, Bi-LSTM and GRU

Yıl 2021, , 341 - 347, 31.12.2021

Mustafa Yurtsever

https://doi.org/10.31590/ejosat.959405

Cited By: 3

Öz

Due to the multifactorial and non-linear nature of the gold market, it is difficult to predict the gold price. The gold price is affected by many external factors, such as market environment, economic crises, oil price increases, tax advantages and interest rates. Therefore, multivariate models can better predict the gold price than univariate models. This study investigated the effects of gold price, crude oil price, exchange rate index, stock market index, and interest indicators between 2001 and 2021. Models created using LSTM, Bi-LSTM and GRU methods were evaluated using lowest Root Mean Square Error (RMSE), Mean Absolute Percent Error (MAPE) and Mean Absolute Error (MAE) metrics. The LSTM model performed best, with 3.48 MAPE, 61,728 RMSE and 48.85 MAE values.

Anahtar Kelimeler

Gold Price, LSTM, Bi-LSTM, GRU

LSTM, Bi-LSTM ve GRU ile Altın Fiyatı Tahmini

Öz

Altın piyasasının çok faktörlü ve doğrusal olmayan özelliği nedeniyle altın fiyatının tahminini yapmak zordur. Altın fiyatı, piyasa ortamı, ekonomik kriz, petrol fiyatlarındaki artış, vergi avantajları, faiz oranları gibi dış faktörlerden etkilenmektedir. Bu nedenle çok değişkenli modeller altın fiyatını tahmin etmede daha iyi sonuçlar verebilmektedir. Makalede, 2001–2021 yılları arasında altın fiyatı, ham petrol fiyatı, döviz kuru endeksi, borsa endeksi ve faiz göstergeleri kullanılmıştır. LSTM, Bi-LSTM ve GRU yöntemleri kullanılarak oluşturulan modeller en düşük Kök Ortalama Kare Hata (RMSE), Ortalama Mutlak Yüzde Hata (MAPE) ve Ortalama Mutlak Hata (MAE) metrikleri ile değerlendirilmiştir. LSTM modeli 3,48 MAPE, 61,728 RMSE ve 48,85 MAE değerleri ile en iyi yöntem olmuştur.

Anahtar Kelimeler

Altın Fiyatı, LSTM, Bi-LSTM, GRU

Kaynakça

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