Peanut oil price change forecasts through the neural network
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
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