IDEAS home Printed from https://ideas.repec.org/a/ibn/masjnl/v10y2016i4p173.html
   My bibliography  Save this article

Application of Artificial Neural Network Model in Predicting Price of Milk in Iran

Author

Listed:
  • Ghorban Shahriary
  • Yaser Mir

Abstract

Changing economic welfare is one of the most important parameters considered by politicians in applying economic policies in agricultural sector. Modifying expenditures is a factor that influences on producers and consumers` economic welfare. Due to the significant impact it has on nutrition and food, job and income of society, milk is a product that is supported by Iranian government. Objective of the research is to predict price of farm gate milk by applying ARIMA and Artificial Neural Networks (ANN). Data from February 2006 to March 2013 were collected from Bureau of Animal Husbandry and Agriculture Support of Iran. The data used had the ability of prediction. Econometric criteria such as , MAD, MAPE and RMSE were also used in order to compare ARIMA error prediction. The results indicate that ANN demonstrated minor error for predicting milk price in a five-month time horizon and it is more accurate than the ARIMA method. Both models predict high fluctuations in milk price as a result of high production risk existing in livestock sector of Iran.

Suggested Citation

  • Ghorban Shahriary & Yaser Mir, 2016. "Application of Artificial Neural Network Model in Predicting Price of Milk in Iran," Modern Applied Science, Canadian Center of Science and Education, vol. 10(4), pages 173-173, April.
  • Handle: RePEc:ibn:masjnl:v:10:y:2016:i:4:p:173
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/mas/article/download/55761/30593
    Download Restriction: no

    File URL: https://ccsenet.org/journal/index.php/mas/article/view/55761
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jean-François Verne, 2022. "Forecast the inflation rate in Lebanon: The use of the artificial neural networks method," Economics Bulletin, AccessEcon, vol. 42(4), pages 1798-1810.
    2. repec:caa:jnljfs:v:preprint:id:111-2023-jfs is not listed on IDEAS

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:masjnl:v:10:y:2016:i:4:p:173. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.