IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-894-3_27.html

Intelligent Price Forecasting System for Spice Traders with Machine Learning

In: Proceedings of the International Conference on Policies, Processes and Practices for Transforming Underdeveloped Economies into Developed Economies (PPP-UD 2025)

Author

Listed:
  • Kiran Basavannappagowda

    (REVA Academy for Corporate Excellence -RACE, REVA University Rukmini Knowledge Park)

  • J. B. Simha

    (REVA Academy for Corporate Excellence -RACE, REVA University Rukmini Knowledge Park)

  • M. P. Praveen

    (Numentrix Consulting LLP, Director – Operations)

  • Gundlupet Sadananda Murthy

    (Director- samparkbindhu)

Abstract

Spices are essential agricultural products that hold considerable economic importance and fulfill various roles in culinary, medicinal, and industrial fields. Black pepper, a spice traded worldwide, experiences frequent price changes due to seasonal variations, inconsistent quality, and disruptions in the supply chain. This research introduces a forecasting model aimed at predicting black pepper prices in local markets of Karnataka. Conventional techniques such as Simple Moving Average (SMA), Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) often yield subpar results when faced with irregular data conditions. To overcome this challenge, a Multiple Linear Regression (MLR) model with lag features was developed, utilizing domain-specific feature engineering. The data processing pipeline included steps for managing missing values, outliers, normalization, and identifying temporal patterns. A knowledge-driven nearest neighbor analysis was employed to improve forecasting accuracy. Among all the models assessed, the MLR model recorded the lowest Mean Absolute Percentage Error (MAPE) of 0.22%. The proposed work also features a user interface designed to aid traders in making informed decisions and allows for a more in-depth analysis of black pepper trading trends.

Suggested Citation

  • Kiran Basavannappagowda & J. B. Simha & M. P. Praveen & Gundlupet Sadananda Murthy, 2025. "Intelligent Price Forecasting System for Spice Traders with Machine Learning," Advances in Economics, Business and Management Research, in: Anuradha Jain & Sachin Gupta (ed.), Proceedings of the International Conference on Policies, Processes and Practices for Transforming Underdeveloped Economies into Developed Economies (P, pages 376-386, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-894-3_27
    DOI: 10.2991/978-94-6463-894-3_27
    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
    for a similarly titled item that would be available.

    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:spr:advbcp:978-94-6463-894-3_27. 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.