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

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Intelligent Price Forecasting System for Spice Traders with Machine Learning

Authors
Kiran Basavannappagowda1, *, J. B. Simha1, M. P. Praveen2, Gundlupet Sadananda Murthy3
1REVA Academy for Corporate Excellence -RACE, REVA University Rukmini Knowledge Park, Kattigenahalli, Yelahanka, Bengaluru, India, 560 064
2Director – Operations, Numentrix Consulting LLP, #332, VI Main, I Stage, KHB Colony, Basaveshwaranagar, Bangalore, India, 560079
3Director- samparkbindhu, J P Nagar 9th phase, Bengaluru, India, 560 062
*Corresponding author. Email: Kiran.AI07@race.reva.edu.in
Corresponding Author
Kiran Basavannappagowda
Available Online 10 November 2025.
DOI
10.2991/978-94-6463-894-3_27How to use a DOI?
Keywords
Black Pepper; Commodity Price Forecasting; Deep Learning; Time Series Analysis; MLR; Lag feature; SMA; ARIMA; LSTM; GRU; Machine Learning; Price Volatility; Market Prediction
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.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Policies, Processes and Practices for transforming Underdeveloped Economies into Developed Economies (PPP-UD 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
10 November 2025
ISBN
978-94-6463-894-3
ISSN
2352-5428
DOI
10.2991/978-94-6463-894-3_27How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Kiran Basavannappagowda
AU  - J. B. Simha
AU  - M. P. Praveen
AU  - Gundlupet Sadananda Murthy
PY  - 2025
DA  - 2025/11/10
TI  - Intelligent Price Forecasting System for Spice Traders with Machine Learning
BT  - Proceedings of the International Conference on Policies, Processes and Practices for transforming Underdeveloped Economies into Developed Economies (PPP-UD 2025)
PB  - Atlantis Press
SP  - 376
EP  - 386
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-894-3_27
DO  - 10.2991/978-94-6463-894-3_27
ID  - Basavannappagowda2025
ER  -