Modelling and forecasting iron ore prices

In this study, the factors affecting iron ore prices are analyzed, and price forecasting models are developed using iron ore as a univariate time series. For forecasting, ETS, ARIMA, XGBoosting, and a hybrid model combining these approaches are utilized. All models are constructed and implemented within the R environment, and their outputs are derived through R. At the end of the study, the models are compared, and their predictive performances are critically evaluated. The XGBoosting model has delivered stronger results compared to the other models. Keywords: Iron ore, Price forecasting, Forecasting models, ETS, ARIMA, XGBoosting