IDEAS home Printed from https://ideas.repec.org/a/ags/injagm/399877.html

Groundnut price forecasting in Gondal market of Gujarat: A comparison of arima and ann models

Author

Listed:
  • Suthar, Bhoomi
  • Pundir, R. S.
  • Popat, Raj

Abstract

The study makes an effort to predict groundnut prices at the Gondal market of Gujarat. Data of monthly groundnut prices from 2002 to 2019 were used in the study. Out of all the markets in Gujarat, Gondal market was considered in the study based on the highest triennium average of total arrivals in last three years of study period. The data were split into 80:20 for training and testing purpose. Time series models namely ARIMA (Autoregressive Integrated Moving Average) methodology given by Box and Jenkins and ANN (Artificial Neural Network) have been used for forecasting prices of groundnut. The results revealed that ANN model (58-1) out performed ARIMA (3,1,2) model on the basis of lowest RMSE and MAPE. As a result, employing the ANN model to forecast groundnut prices is very helpful not only for farmers but also for developing policies and enhancing groundnut marketing effectiveness. The farmers were advised to make appropriate marketing decisions.

Suggested Citation

  • Suthar, Bhoomi & Pundir, R. S. & Popat, Raj, 2023. "Groundnut price forecasting in Gondal market of Gujarat: A comparison of arima and ann models," Indian Journal of Agricultural Marketing, Indian Society of Agricultural Marketing, vol. 37(1).
    • Handle: RePEc:ags:injagm:399877
    DOI: 10.22004/ag.econ.399877
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/399877/files/Groundnut%20price%20forecasting%20in%20Gondal%20market%20of%20Gujarat.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.399877?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;

    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:ags:injagm:399877. 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: AgEcon Search (email available below). General contact details of provider: https://agrilmktg.in/ .

    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.