## 1 Introduction  
The market of iron ore with its numerous agents and contributors has been affected by different conditions and variables. Small and large producers, permanent and seasonal exporters and therefore, different agents have been active in this industry. Different agents consider not only product price but also 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. So, 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 in controlling steel price [2]. Iron ore has previously confirmed itself to be a substantial resource in the economy and society [3]. More than half of the worldwide extraction of metal ores is related to iron ore and copper ore [4]. Recently, the expansive substructure buildings in the world increased its demand for iron ore [5]. Furthermore, iron ore mining industry plays an important role in the economic growth of developed countries [6]. In this regard, it should be noted that iron ore consumption in China increased by 784% from 2000 to 2010 [7]. The interest in forecasting of the iron ore price meaningfully increased after an alternatively fluctuation since 2008 (Figure 1) [8]. Therefore, major steel producers, provide their own strategic raw materials through long-term contracts [9]. At the moment, pricing of iron ore is based on seasonal agreements of major producers of iron ore and major consumers of steel so that the two markets of Europe and Eastern Asia (in past, Japan and Now, China) have significant role in internal iron ore deals. A major share of distributed iron ore is produced by the three companies of Vale (a semi-private company) (Rio de Janeiro, Brazil ), Rio Tinto(London, UK), and B.H.P Billiton(Melbourne, Australia). Because of the fact that each ton of crude steel is made by using 1.5 ton of iron ore, production of crude steel can be introduced as a demand agent and because provision of iron ore is almost exclusive, producers provide the market with an output totally in association with instant demand [10]. Artificial intelligence, soft computing (SC), and machine learning (ML) techniques such as artificial neural network (ANN) and fuzzy inference system (FIS) have been developed as non-linear tools for solving problems in science and engineering specially in civil and mining fields [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48]. The use of these techniques in different applications of civil and mining engineering e.g., water and environmental resources [49; 50], geotechnical fields [51; 52; 53; 54; 55; 56], structural and material [13; 57; 58; 59; 60], blasting environmental issues [61; 62; 63; 64; 65], mineral processing and pricing [7; 11; 66], transportation planning and design [67; 68]. In fact, further engineers and researchers are able to utilize these techniques to get a high level of accuracy for engineering problems. In the present paper, a new model in the field of mineral price prediction namely group method of data handling (GMDH) is introduced. Literature shows that the models that work on the basis of self-organizing networks containing active neurons (GMDH) are of a higher effectiveness in terms of making more accurate and less labor-intensive predictions. In addition, the paper evaluates the  
Figure 1: Monthly variation of iron ore price. * Description: China import Iron Ore Fines 62% FE spot (CFR Tianjin port).