Forecasting the global price of iron ore using neural networks

Today's dependence on technology increases human need for iron ore products, and forecasts suggest that by the year 2035 Demand for steel 60 Percent increase ( Mohammadi , Soltani Mohammadi , and the donor of security 2035) For this reason, predicting the price of metals such as iron ore using quantitative and qualitative methods such as economic and technical study The market didn't really match the reality One of the most common methods of pricing is the time series method. In this research, by modeling and using series analysis At the time, with the help of dynamic neural network, the price of iron ore has been predicted. Then, using the neural network method Synthetic and given the monthly price of iron ore and the factors affecting its volatility, the estimated iron ore price and then the results the obtained results were evaluated in terms of predictability. Optimal neural network model with 3 Layer and 10 Near estimated the price of iron ore very accurately. In this model the amount of training error is approx. %1.7 And for equal validation %2.3 And test error %1.5. Also, regression and Data correlation at 32% confidence level and a high correlation value With R2= 0.98 Indicates a good and accurate model