Research on Ginger Price Prediction Model Based on Deep Learning
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 mech...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/6/596 |
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| author | Fengyu Li Xianyong Meng Ke Zhu Jun Yan Lining Liu Pingzeng Liu |
| author_facet | Fengyu Li Xianyong Meng Ke Zhu Jun Yan Lining Liu Pingzeng Liu |
| author_sort | Fengyu Li |
| collection | DOAJ |
| description | 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<sup>2</sup>) 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. |
| format | Article |
| id | doaj-art-b6e0950dbd2d4d218870d490eedabd98 |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-b6e0950dbd2d4d218870d490eedabd982025-08-20T02:11:11ZengMDPI AGAgriculture2077-04722025-03-0115659610.3390/agriculture15060596Research on Ginger Price Prediction Model Based on Deep LearningFengyu Li0Xianyong Meng1Ke Zhu2Jun Yan3Lining Liu4Pingzeng Liu5School of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaIn 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<sup>2</sup>) 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.https://www.mdpi.com/2077-0472/15/6/596ginger priceprice predictionLSTM networkKAN networkAMP-PSO algorithm |
| spellingShingle | Fengyu Li Xianyong Meng Ke Zhu Jun Yan Lining Liu Pingzeng Liu Research on Ginger Price Prediction Model Based on Deep Learning Agriculture ginger price price prediction LSTM network KAN network AMP-PSO algorithm |
| title | Research on Ginger Price Prediction Model Based on Deep Learning |
| title_full | Research on Ginger Price Prediction Model Based on Deep Learning |
| title_fullStr | Research on Ginger Price Prediction Model Based on Deep Learning |
| title_full_unstemmed | Research on Ginger Price Prediction Model Based on Deep Learning |
| title_short | Research on Ginger Price Prediction Model Based on Deep Learning |
| title_sort | research on ginger price prediction model based on deep learning |
| topic | ginger price price prediction LSTM network KAN network AMP-PSO algorithm |
| url | https://www.mdpi.com/2077-0472/15/6/596 |
| work_keys_str_mv | AT fengyuli researchongingerpricepredictionmodelbasedondeeplearning AT xianyongmeng researchongingerpricepredictionmodelbasedondeeplearning AT kezhu researchongingerpricepredictionmodelbasedondeeplearning AT junyan researchongingerpricepredictionmodelbasedondeeplearning AT liningliu researchongingerpricepredictionmodelbasedondeeplearning AT pingzengliu researchongingerpricepredictionmodelbasedondeeplearning |