Forecasting Green Chilli Prices: Using Data Analytics to Gain Market Understanding

Kumar, Midathana Anil and Ghosh, Arunava and ., Vinay H T and Poddar, Parthendu and Reza, Md. Wasim (2024) Forecasting Green Chilli Prices: Using Data Analytics to Gain Market Understanding. Journal of Scientific Research and Reports, 30 (10). pp. 671-679. ISSN 2320-0227

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Abstract

Green chilli is a commercially significant vegetable crop grown year-round due to its high demand for both nutritional and health benefits. India stands as the largest producer and consumer of chilli globally, with West Bengal leading in area under cultivation and ranking sixth in production. This study aims to compare and identify the most accurate model for forecasting green chilli prices in the Haldibari market of Cooch Behar district, West Bengal. Price data from January 2015 to May 2024, sourced from AGMARKNET, was used for model development, with 85% of the data allocated for training and 15% for validation. The models tested include the traditional Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and machine learning models like Artificial Neural Networks (ANN) and Support Vector Regression (SVR). Among these, the ANN model is found to be the most accurate, with a low Mean Absolute Percentage Error (MAPE) of 0.14. The model is used to forecast prices for the next 12 months, up to May 2025. The study’s findings can aid farmers and policymakers in creating effective crop planning strategies, helping to boost local farm income.

Item Type: Article
Subjects: Archive Digital > Multidisciplinary
Depositing User: Unnamed user with email support@archivedigit.com
Date Deposited: 21 Oct 2024 09:51
Last Modified: 21 Oct 2024 09:51
URI: http://eprints.ditdo.in/id/eprint/2337

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