## 2 Literature Review  
Up to now, different techniques such as statistical and artificial intelligence methods have been suggested for prediction of iron ore price [7]. Traditional mathematical models such as time series [11; 69] and the multi linear regression [70; 71] models have been used for metals price prediction. One of the most common traditional mathematical models for predicting time series is the autoregressive integrated moving average (ARIMA) method. This technique is one of the most routinely used statistical approaches for time series predicting over the past decades. ARIMA have relished beneficial applications in forecasting insurance, economics, energy, engineering, social, stock problems, and foreign exchange [72; 73; 74; 75]. Critical literature review show that the accuracy of SC and ML techniques is superior to the ARIMA models in predicting time series problems [76; 77; 78; 79; 80].  
As stated before, the applications of ML and SC techniques have been widely used in fields of civil and mining engineering. In terms of metal and material price prediction, several researchers highlighted a successful implementation of such techniques. For example, [PERSON] et al. [11] used and developed an ANN model for the prediction of variation of gold prices. They considered the role of ward network in the architecture of the proposed ANN and reported a high ability of the ANN model in forecasting gold price. The monthly copper price was predicted by [PERSON] and [PERSON] [81] proposing ANN and FIS predictive models. They constructed many soft computing models of ANN and FIS and finally concluded that FIS is a more powerful technique in predicting copper price compared to ANN model. In another study, [PERSON] et al. [82] forecasted a long-term monthly gold price fluctuations by using four ANN-based models i.e., particle swarm optimization (PSO)-ANN, genetic algorithm (GA)-ANN, and whale optimization algorithm (WOA)-ANN and grey wolf optimization-ANN. In fact, they studied the power of these hybrid models in predicting gold price fluctuations and finally, they introduced WOA-ANN as a new and applicable model in this field. [PERSON] et al. [2] proposed a hybrid intelligent model i.e., chaotic grasshopper optimization algorithm-ANN for estimation of iron ore prices and concluded that their proposed model is a promising technique for forecasting commodity prices with high accuracy. In another study of price prediction, the prediction of coal price fluctuations was considered as the objective by [PERSON] et al. [83]. They developed three models namely support vector machine (SVM), ANN, and deep neural network (DNN) with the aim to successfully show that the DNN model is able to provide higher performance capacity in estimating coal price fluctuations.  
According to the above discussion, the authors decide to use and propose the GMDH which is considered as a new intelligent system in prediction of iron ore price. For comparison purposes, a traditional statistical model, i.e., ARIMA, and three ML techniques i.e., SVR, CART, and ANN are selected to be applied for iron ore price prediction. Then, an evaluation is performed between the applied models and the best model is selected and introduced in field of iron ore price prediction.