Research on Ginger Price Prediction Model Based on Deep Learning

Fengyu Li, Xianyong Meng (), Ke Zhu, Jun Yan, Lining Liu and Pingzeng Liu ()
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Fengyu Li: School of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
Xianyong Meng: School of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
Ke Zhu: School of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
Jun Yan: School of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
Lining Liu: School of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
Pingzeng Liu: School of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China

Agriculture, 2025, vol. 15, issue 6, 1-23

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 (R 2 ) 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 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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