Hybrid Deep Learning Models For Gold Price Prediction: Enhancing Forecast In Volatile Financial Markets
TL;DR
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 is provided, as it showed the capability to detect short-term trends and long-term sequential gaps in gold price series.
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