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

机译:使用不同的混合智能系统预测15天聚酯切片价格的比较研究

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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.
机译:风险情况下的预测对于管理人员协助他们进行决策是非常重要的功能。证券市场中波动较大的市场之一是化学市场。在这项研究中,预测的目标项目是PET(聚对苯二甲酸乙二酯),PET是纺织工业的原材料,它对油价以及供求比率非常敏感。主要思想是通过Lee和Liu [1]提出的NORN模型得出的。在修改了NORN模型后,本文提出了一个模型,并将实际数据应用于该新模型(我们将其命名为AHIS,代表自适应混合智能系统)。最后,进行了三种不同类型的仿真并将其相互比较。他们表明,同时支持模糊系统和神经网络概念的混合模型,极大地满足了研究问题。在正常情况下,该模型可以预测相关趋势,并且可以用作经理的DSS。

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