Gold Commodity Price Prediction Using Tree-based Prediction Models
Keywords:
Commodity Price, Gold, Machine Learning, Price Prediction, Tree-based ModelAbstract
Commodity price forecasting is always a matter of research for practitioners and academia as a non-linear price structure of commodity with large volatility is associated with it. The efficiency of price prediction systems has been proven with the recent expansion of Artificial Intelligence and enhanced capabilities of computational equipment. Machine Learning (ML) is widely used in predicting the prices across the markets. Though a variety of ML methods are in use to predict commodity prices in recent times, this paper attempts to predict gold commodity closing prices specifically using tree-based models including Decision Tree, Adaptive Boosting (AdaBoost), Random Forest, Gradient Boosting, and eXtreme Gradient Boosting (XGBoost). The inputs to each of the prediction models were chosen from a total of nine technical indicators such as Simple 10-day moving average, Weighted 14-day moving average, Momentum, Stochastics K%, Stochastic D%, Relative Strength Index (RSI), William’s R%, Moving Average Convergence Divergence (MACD) and Commodity Channel Index (CCI) and four metrics namely RMSE, MAE, MSE and R2 were analysed for each technique of all the tree-based models considered and which were internally competing to explain superior forecast. All four metrics were calculated to check the effectiveness of different prediction models.
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