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

  • Mojtaba Sedigh Fazli

    ()

    (Centre de Recherche Magellan - Université Jean Moulin - Lyon III : EA3713)

  • Jean-Fabrice Lebraty

    ()

    (Centre de Recherche Magellan - Université Jean Moulin - Lyon III : EA3713)

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.

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Paper provided by HAL in its series Post-Print with number hal-00859445.

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Date of creation: Aug 2013
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Publication status: Published - Presented, International Joint Conference on Neural Networks, 2013, Dallas, Texas, United States
Handle: RePEc:hal:journl:hal-00859445
Note: View the original document on HAL open archive server: http://hal-univ-lyon3.archives-ouvertes.fr/hal-00859445
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