A solution for forecasting pet chips prices for both short-term and long-term price forcasting, using genetic programming
Nowadays, forecasting what will happen in economic environments plays a crucial role. We showed that in PET market how a neuro-fuzzy hybrid model can assist the managers in decision-making. In this research, the target is to forecast the same item through another intelligent tool which obeys the evolutionary processing mechanisms. Again, the item for prediction here is PET (Poly Ethylene Terephthalate) which is the raw material for textile industries and it is highly sensitive against oil price fluctuations and also some other factors such as the demand and supply ratio. The main idea is coming through AHIS model which was presented by Mojtaba Sedigh Fazli and J.F. Lebraty in 2013. In this communication, the hybrid module is substituted with genetic programming. Finally, the simulation has been conducted and compared to three different answers which were presented before the results show that Genetic programming results (acting like hybrid model) which support both Fuzzy Systems and Neural Networks, satisfy the research question considerably.
|Date of creation:||Jul 2013|
|Publication status:||Published in WorldComp'2013. The 2013 International Conference on Artificial Intelligence, Jul 2013, Las Vegas, Nevada, United States. CSREA Press, II, pp.631-637, 2013|
|Note:||View the original document on HAL open archive server: https://hal-univ-lyon3.archives-ouvertes.fr/hal-00859457|
|Contact details of provider:|| Web page: https://hal.archives-ouvertes.fr/|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Mojtaba Sedigh Fazli & Jean-Fabrice Lebraty, 3/08. "A comparative study on forecasting polyester chips prices for 15 days, using different hybrid intelligent systems," Post-Print hal-00859445, HAL.
When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-00859457. 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 references are entirely missing, you can add them using this form.