Forecasting Agricultural Commodity Prices Using Multilayer Perceptron Neural Networks

Authors

  • Rubi Kambo, Jyothi Pillai, and Sunita Soni

Keywords:

Agriculture Commodity, Tomato, MLP, Price Forecasting

Abstract

Transitions in the supply and demand for a good cause pricechanges, which in turn impact customers’ disposable incomes. If a precise
or somewhat accurate forecast can be made of the price of the commodity,then the risk associated with price fluctuations may be managed. In thisresearch, a model is employed using MLP to accurately anticipate agri-

References

Agriculture & Food Management.chapter-7,” Information is provided by Ministry of Finance., Government of India, Economic Survey 2021-22 234-264.

Nowrouz Kohzadi, Milton S. Boyd, Bahman Kermanshahi, and Iebling Kaastra, ”A comparison of artificial neural network and time-series model for forecasting commodity prices,” NeuroComputing.

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Published

2024-07-20

How to Cite

Rubi Kambo, Jyothi Pillai, and Sunita Soni. (2024). Forecasting Agricultural Commodity Prices Using Multilayer Perceptron Neural Networks. Journal of Computational Analysis and Applications (JoCAAA), 33(07), 2515–2547. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3569

Issue

Vol. 33 No. 07 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.