A comparative study on forecasting polyester chips prices for 15 days, using different hybrid intelligent systems
AbstractForecasting 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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Bibliographic InfoPaper provided by HAL in its series Post-Print with number hal-00859445.
Date of creation: Aug 2013
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Publication status: Published - Presented, International Joint Conference on Neural Networks, 2013, Dallas, Texas, United States
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Efficient Market Hypothesis; Financial Forecasting; Chemicals; Artificial Intelligence; Artificial Neural Networks; Decision Support System; Locally Linear Model Tree; Hybrid Neuro Fuzzy Model.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-09-26 (All new papers)
- NEP-CMP-2013-09-26 (Computational Economics)
- NEP-FOR-2013-09-26 (Forecasting)
- NEP-ORE-2013-09-26 (Operations Research)
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- 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 hal-00859457, HAL.
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