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

Author

Listed:
  • Mojtaba Sedigh Fazli

    (Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - Lyon)

  • Jean-Fabrice Lebraty

    (Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - Lyon)

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.

Suggested Citation

  • Mojtaba Sedigh Fazli & Jean-Fabrice Lebraty, 2013. "A comparative study on forecasting polyester chips prices for 15 days, using different hybrid intelligent systems," Post-Print hal-00859445, HAL.
    • Handle: RePEc:hal:journl:hal-00859445
    Note: View the original document on HAL open archive server: https://univ-lyon3.hal.science/hal-00859445
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    Cited by:

    1. Mojtaba Sedigh Fazli & Jean-Fabrice Lebraty, 2013. "A solution for forecasting pet chips prices for both short-term and long-term price forcasting, using genetic programming," Post-Print hal-00859457, HAL.

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