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

Mojtaba Sedigh Fazli () and Jean-Fabrice Lebraty

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Abstract: Forecasting in a risky situation is a very important function for managers to assist 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 its very sensitive on oil prices and the demand and supply ratio. The main idea is coming through NORN model which was presented by T. Lee and James N.K. Liu in 2001. 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 together, which 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.

Keywords: Hybrid Neuro Fuzzy Model; Efficient Market Hypothesis; Financial Forecasting; Chemicals; Artificial Intelligence; Artificial Neural Networks; Decision Support System; Locally Linear Model Tree; Hybrid Neuro Fuzzy Model. (search for similar items in EconPapers)
Date: 2013-08
New Economics Papers: this item is included in nep-cmp, nep-for and nep-ore
Note: View the original document on HAL open archive server: https://hal-univ-lyon3.archives-ouvertes.fr/hal-00859445
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Published in International Joint Conference on Neural Networks, Aug 2013, Dallas, Texas, United States. pp.1869-1875

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