Gold Price Forecasting Using LSTM, Bi-LSTM and GRU

Mustafa Yurtsever

32 citations0/0 files storedEuropean Journal of Science and Technology

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Stock Market Forecasting Methods Forecasting Techniques and Applications Market Dynamics and Volatility

Abstract

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.

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2003Time series forecasting using a hybrid ARIMA and neural network model 2020A CNN–LSTM model for gold price time-series forecasting 2013Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold 2010An overview of global gold market and gold price forecasting 2019Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model 2018Deep Learning with Gated Recurrent Unit Networks for Financial Sequence Predictions 2004GOLD AS AN INFLATION HEDGE?2020A Gated Recurrent Unit Approach to Bitcoin Price Prediction

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