IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-030-77485-1_4.html
   My bibliography  Save this book chapter

Commodity Markets: Machine Learning Techniques

In: Machine Learning and Artificial Intelligence for Agricultural Economics

Author

Listed:
  • Chandrasekar Vuppalapati

    (San Jose State University)

Abstract

In this chapter, machine learning techniques are introduced to perform historical commodity analysis to derive leading economic macro and micro indicators that would provide a comprehensive market view to small farmers. As part of the chapter, demand and supply framework is introduced. Next, commodity stocks to use ratio indictor are introduced and the impact of stocks to use ratio on the pricing model developed. Time series techniques are introduced to validate trend, seasonality, and stationarity of the time series. Next, the time series feature causation and correlation models and vector autoregression (VAR) are introduced. Finally, the chapter presents two time series-based use cases: predicting gold commodity prices and worldwide study of fertilizer price predict.

Suggested Citation

  • Chandrasekar Vuppalapati, 2021. "Commodity Markets: Machine Learning Techniques," International Series in Operations Research & Management Science, in: Machine Learning and Artificial Intelligence for Agricultural Economics, chapter 0, pages 219-327, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-77485-1_4
    DOI: 10.1007/978-3-030-77485-1_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    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:spr:isochp:978-3-030-77485-1_4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.