A comparative study on forecasting polyester chips prices for 15 days, using different hybrid intelligent systems

Forecasting in a risky situation is a very important function for managers to assist them in decision-making. One of the fluctuated markets in stock exchange market is chemical market. In this research the target item for prediction is PET (Poly Ethylene Terephthalate) which is the raw material for textile industries and it´s very sensitive on oil prices and the demand and supply ratio. The main idea is coming through NORN model which was presented by Lee and Liu [1]. In this article after modifying the NORN model, a model has been proposed and real data are applied to this new model (we named it AHIS which stands for Adaptive Hybrid Intelligent System). Finally, three different types of simulation have been conducted and compared with each other. They show that hybrid model which is supporting both Fuzzy Systems and Neural Networks concepts, satisfied the research question considerably. In normal situation the model forecasts a relevant trend and can be used as a DSS for a manager.