A COMPARATIVE STUDY OF DEEP LEARNING MODELS FOR COTTON PRICE FORECASTING IN GUJARAT, INDIA

G. Y. Chandan and Dr. A. N. Khokhar

International Journal of Agriculture and Environmental Research, 2025, vol. 11, issue 01

Abstract: Forecasting agricultural prices is exceptionally difficult because of the unpredictable global weather events, influential role of government policies, evolving consumer preferences and technology. This study focuses on the impact of price forecasting in the agricultural sector, specifically within the cotton industry. Accurate predictions of cotton prices are of most important to various stakeholders, including cotton farmers, textile mills and shippers. To enhance forecasting accuracy, this research employs advanced machine learning techniques such as Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), as well as stacked LSTM models. These models are trained and evaluated using statistical metrics like RMSE, MAPE, SMAPE and MAE. Notably, the stacked LSTM model consistently outperforms other models, demonstrating superior predictive performance with minimal errors. This study also highlights the stacked LSTM's ability to effectively capture long-term data dependencies, leading to significantly improved prediction precision.

Keywords: Agricultural; Finance (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/355578/files/ijaer_11__15.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:ijaeri:355578

DOI: 10.22004/ag.econ.355578

Access Statistics for this article

More articles in International Journal of Agriculture and Environmental Research from Malwa International Journals Publication
Bibliographic data for series maintained by AgEcon Search ().