Forecasting monthly copper price: A comparative study of various machine learning-based methods

Abstract

Copper is one of the valuable natural resources, and it was widely used in many different industries. The complicated fluctuations of copper prices can significantly affect other industries. Therefore, this study aims to develop and propose several forecast models for forecasting monthly copper prices in the future based on various algorithms in machine learning, including multi-layer perceptron (MLP) neural network, k-nearest neighbors (KNN), support vector machine (SVM), gradient boosting tree (GBT), and random forest (RF). The monthly copper price dataset from January 1990 to December 2019 was collected for this aim based on other metals and natural gas prices. In addition, the influence of currency exchange rates of the countries that have the largest copper production around the world was also taken into account and used as input variables for forecasting copper price. The different set of predictors (t, t-1, t-2, t-3, t-4. t-5) were considered to forecast monthly copper prices based on the mentioned machine learning techniques. The results revealed that the currency exchange rates of the countries that have the most abundant copper production around the world have a significant effect on the volatility of monthly copper prices in the world, and they should be used to forecast monthly copper prices in the future. A comprehensive comparison of various machine learning techniques shows that MLP neural network (with deep learning techniques) is the best method for forecasting monthly copper price with an MAE of 228.617 and RMSE of 287.539. Whereas, the other models, such as SVM, RF, KNN, and GBT, provided higher errors with an MAE in the range of 308.691–453.147, RMSE in the range of 393.599–552.208. In this sense, MLP neural network can be used as a reliable tool to forecast copper prices in the future.