Research on Ginger Price Prediction Model Based on Deep Learning

Li, Fengyu; Meng, Xianyong; Zhu, Ke; Yan, Jun; Liu, Lining; Liu, Pingzeng

doi:10.3390/agriculture15060596

Open AccessArticle

Research on Ginger Price Prediction Model Based on Deep Learning

by

Fengyu Li

^1,2,3,

Xianyong Meng

^1,2,3,*

,

Ke Zhu

^1,2,3,

Jun Yan

^1,2,3,

Lining Liu

^1,2,3 and

Pingzeng Liu

^1,2,3,*

¹

School of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China

²

Key Laboratory of Huanghuaihai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Tai’an 271018, China

³

Agricultural Big Data Research Center, Shandong Agricultural University, Tai’an 271018, China

^*

Authors to whom correspondence should be addressed.

Agriculture 2025, 15(6), 596; https://doi.org/10.3390/agriculture15060596

Submission received: 11 February 2025 / Revised: 1 March 2025 / Accepted: 6 March 2025 / Published: 11 March 2025

(This article belongs to the Special Issue Computational, AI and IT Solutions Helping Agriculture)

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Abstract

In order to ensure the price stability of niche agricultural products and enhance farmers’ income, the study delves into the pattern of the ginger price fluctuation rule and its main influencing factors. By combining seasonal decomposition STL, long and short-term memory network LSTM, attention mechanism ATT and Kolmogorov-Arnold network, a combined STL-LSTM-ATT-KAN prediction model is developed, and the model parameters are finely tuned by using multi-population adaptive particle swarm optimisation algorithm (AMP-PSO). Based on an in-depth analysis of actual data on ginger prices over the past decade, the STL-LSTM-ATT-KAN model demonstrated excellent performance in terms of prediction accuracy: its mean absolute error (MAE) was 0.111, mean squared error (MSE) was 0.021, root mean squared error (RMSE) was 0.146, and the coefficient of determination (R²) was 0.998. This study provides the Ginger Industry, agricultural trade, farmers and policymakers with digitalised and intelligent aids, which are important for improving market monitoring, risk control, competitiveness and guaranteeing the stability of supply and price.

Keywords: ginger price; price prediction; LSTM network; KAN network; AMP-PSO algorithm

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MDPI and ACS Style

Li, F.; Meng, X.; Zhu, K.; Yan, J.; Liu, L.; Liu, P. Research on Ginger Price Prediction Model Based on Deep Learning. Agriculture 2025, 15, 596. https://doi.org/10.3390/agriculture15060596

AMA Style

Li F, Meng X, Zhu K, Yan J, Liu L, Liu P. Research on Ginger Price Prediction Model Based on Deep Learning. Agriculture. 2025; 15(6):596. https://doi.org/10.3390/agriculture15060596

Chicago/Turabian Style

Li, Fengyu, Xianyong Meng, Ke Zhu, Jun Yan, Lining Liu, and Pingzeng Liu. 2025. "Research on Ginger Price Prediction Model Based on Deep Learning" Agriculture 15, no. 6: 596. https://doi.org/10.3390/agriculture15060596

APA Style

Li, F., Meng, X., Zhu, K., Yan, J., Liu, L., & Liu, P. (2025). Research on Ginger Price Prediction Model Based on Deep Learning. Agriculture, 15(6), 596. https://doi.org/10.3390/agriculture15060596

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