IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

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

Listed author(s):
  • Mojtaba Sedigh Fazli


    (Centre de Recherche Magellan - Université Jean Moulin - Lyon III - Institut d'Administration des Entreprises (IAE) - Lyon)

  • Jean-Fabrice Lebraty


    (Centre de Recherche Magellan - Université Jean Moulin - Lyon III - Institut d'Administration des Entreprises (IAE) - Lyon)

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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

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

in new window

Date of creation: Aug 2013
Publication status: Published in IEEE & International Neural Network Society. International Joint Conference on Neural Networks, Aug 2013, Dallas, Texas, United States. pp.1869-1875, 2013
Handle: RePEc:hal:journl:hal-00859445
Note: View the original document on HAL open archive server:
Contact details of provider: Web page:

No references listed on IDEAS
You can help add them by filling out this form.

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-00859445. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.