Abstract
Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques.
Highlights
The market of iron ore with its numerous agents and contributors has been affected by different conditions and variables
The results demonstrated a high level of accuracy of estimating iron ore price
Oil price, steel price, iron ore production, steel production, gold price, inflation rate, exchange rate, interest rate, dowjones stock price, US GDP, aluminum price, China GDP were selected as input variables
Summary
The market of iron ore with its numerous agents and contributors has been affected by different conditions and variables. Permanent and seasonal exporters and different agents have been active in this industry. Different agents consider product price and price of initial ingredients like iron ore, coke, and other influential factors such as oil, transportation fee and demand value of consumptive or alternative products. Rising or falling price of iron ore should be emphasized based on its influence on demand and price of other products or being affected by them [1]. Iron ore is the basic raw material used in steel production. About 10 to 20 percent of total cost of steel is attributed to iron ore so that identification of factors affecting price of iron ore can be beneficial
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