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All Journal Indonesian Journal of Data and Science
Ni Wayan Yeni Pratiwi
Institut Bisnis dan Teknologi Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Hybrid Deep Learning Models For Gold Price Prediction: Enhancing Forecast In Volatile Financial Markets Ni Luh Wiwik Sri Rahayu Ginantra; Ni Wayan Yeni Pratiwi; Christina Purnama Yanti; Wayan Gede Suka Parwita
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.394

Abstract

Gold is viewed as an investment that will remain valuable over the long term and as an investment that will hedge against inflation; however, the volatility of its price in the short term necessitates the use of effective forecasting techniques for investment decisions. This research uses a Hybrid Deep Learning technique, by predicting the price of gold using historical time series data with a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. The model was tested with batch sizes of 16, 32, and 64 using the Adam optimizer with a learning rate of 0.0001 and dropout of 0.2. This research provides an indication of the extent to which gold price forecasting, at least from a financial forecasting perspective, can be achieved using a hybrid model of CNN and LSTM, as it showed the capability to detect short-term trends and long-term sequential gaps in gold price series. The experimental results show out of several performed analyses on the CNN-LSTM model, the one with a batch of 16 showed the best performance as it achieved  an RMSE (Root Mean Square Error) of 11.518535% which implies there was great closeness between the actual gold price and the predicted gold price
Co-Authors Christina Purnama Yanti Ni Luh Wiwik Sri Rahayu Ginantra Wayan Gede Suka Parwita