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Peanut oil price change forecasts through the neural network

Bingzi Jin, Xiaojie Xu, Yun Zhang

Foresight

ISSN: 1463-6689

Article publication date: 16 January 2025

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Abstract

Purpose

For a wide range of market actors, including policymakers, forecasting changes in commodity prices is crucial. As one of essential edible oil, peanut oil’s price swings are certainly important to predict. In this paper, the weekly wholesale price index for the period of January 1, 2010 to January 10, 2020 is used to address this specific forecasting challenge for the Chinese market.

Design/methodology/approach

The nonlinear auto-regressive neural network (NAR-NN) model is the forecasting method used. Forecasting performance based on various settings, such as training techniques, delay counts, hidden neuron counts and data segmentation ratios, are assessed to build the final specification.

Findings

With training, validation and testing root mean square errors of 5.89, 4.96 and 5.57, respectively, the final model produces reliable and accurate forecasts. Here, this paper demonstrates the applicability of the NAR-NN approach for commodity price predictions.

Originality/value

On the one hand, the findings may be used as independent technical price movement predictions. Conversely, they may be included in forecast combinations with forecasts derived from other models to form viewpoints of commodity price patterns for policy research.

Keywords

Acknowledgements

Acknowledgements: not applicable.

Funding: no funding.

Declarations: Availability of data and materials: available upon request.

Competing interests: no competing interests.

Citation

Jin, B., Xu, X. and Zhang, Y. (2025), "Peanut oil price change forecasts through the neural network", Foresight, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/FS-01-2023-0016

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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