Improving trend prediction of agricultural futures price using image encoding and attention mechanisms
Abstract The volatility of agricultural futures prices exerts a profound impact on the global economy, significantly affecting the economic conditions of both agricultural producers and consumers. Accurately predicting the trends of these futures prices is crucial for effective risk management, poli...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Management System Engineering |
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| Online Access: | https://doi.org/10.1007/s44176-025-00042-5 |
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| _version_ | 1850221813237284864 |
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| author | Dabin Zhang Huiqiang Xie Huanling Hu Zehui Yu |
| author_facet | Dabin Zhang Huiqiang Xie Huanling Hu Zehui Yu |
| author_sort | Dabin Zhang |
| collection | DOAJ |
| description | Abstract The volatility of agricultural futures prices exerts a profound impact on the global economy, significantly affecting the economic conditions of both agricultural producers and consumers. Accurately predicting the trends of these futures prices is crucial for effective risk management, policy formulation, and investment decision-making. However, these prices are influenced by a multitude of factors, exhibiting complex fluctuation patterns and strong nonlinear characteristics, which make predictions exceptionally complicated and challenging. This paper introduces a novel hybrid deep learning model named ImgEnc-AttNet, which integrates image encoding techniques with attention mechanisms to enhance the model’s capability to interpret agricultural futures price dynamics. By transforming one-dimensional time series data into image representations, the model employs Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) to extract rich visual patterns and temporal dependencies from the encoded images. It incorporates two types of attention mechanisms to refine feature extraction and capture deeper patterns in price fluctuations. Furthermore, we enhance the model’s feature extraction capabilities by fusing encoded image features with original numerical features, minimizing information loss during data transformation. Experimental results across two datasets demonstrate that ImgEnc-AttNet significantly outperforms benchmark models in both prediction accuracy and robustness. |
| format | Article |
| id | doaj-art-c91f5589330e4d5b85937a34c4a5a8fd |
| institution | OA Journals |
| issn | 2731-5843 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Management System Engineering |
| spelling | doaj-art-c91f5589330e4d5b85937a34c4a5a8fd2025-08-20T02:06:35ZengSpringerManagement System Engineering2731-58432025-06-014112210.1007/s44176-025-00042-5Improving trend prediction of agricultural futures price using image encoding and attention mechanismsDabin Zhang0Huiqiang Xie1Huanling Hu2Zehui Yu3College of Mathematics and Informatics, South China Agricultural UniversityCollege of Mathematics and Informatics, South China Agricultural UniversityCollege of Mathematics and Informatics, South China Agricultural UniversityCollege of Mathematics and Informatics, South China Agricultural UniversityAbstract The volatility of agricultural futures prices exerts a profound impact on the global economy, significantly affecting the economic conditions of both agricultural producers and consumers. Accurately predicting the trends of these futures prices is crucial for effective risk management, policy formulation, and investment decision-making. However, these prices are influenced by a multitude of factors, exhibiting complex fluctuation patterns and strong nonlinear characteristics, which make predictions exceptionally complicated and challenging. This paper introduces a novel hybrid deep learning model named ImgEnc-AttNet, which integrates image encoding techniques with attention mechanisms to enhance the model’s capability to interpret agricultural futures price dynamics. By transforming one-dimensional time series data into image representations, the model employs Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) to extract rich visual patterns and temporal dependencies from the encoded images. It incorporates two types of attention mechanisms to refine feature extraction and capture deeper patterns in price fluctuations. Furthermore, we enhance the model’s feature extraction capabilities by fusing encoded image features with original numerical features, minimizing information loss during data transformation. Experimental results across two datasets demonstrate that ImgEnc-AttNet significantly outperforms benchmark models in both prediction accuracy and robustness.https://doi.org/10.1007/s44176-025-00042-5Agricultural futures priceTime series analysisDeep learningImage encodingAttention mechanism |
| spellingShingle | Dabin Zhang Huiqiang Xie Huanling Hu Zehui Yu Improving trend prediction of agricultural futures price using image encoding and attention mechanisms Management System Engineering Agricultural futures price Time series analysis Deep learning Image encoding Attention mechanism |
| title | Improving trend prediction of agricultural futures price using image encoding and attention mechanisms |
| title_full | Improving trend prediction of agricultural futures price using image encoding and attention mechanisms |
| title_fullStr | Improving trend prediction of agricultural futures price using image encoding and attention mechanisms |
| title_full_unstemmed | Improving trend prediction of agricultural futures price using image encoding and attention mechanisms |
| title_short | Improving trend prediction of agricultural futures price using image encoding and attention mechanisms |
| title_sort | improving trend prediction of agricultural futures price using image encoding and attention mechanisms |
| topic | Agricultural futures price Time series analysis Deep learning Image encoding Attention mechanism |
| url | https://doi.org/10.1007/s44176-025-00042-5 |
| work_keys_str_mv | AT dabinzhang improvingtrendpredictionofagriculturalfuturespriceusingimageencodingandattentionmechanisms AT huiqiangxie improvingtrendpredictionofagriculturalfuturespriceusingimageencodingandattentionmechanisms AT huanlinghu improvingtrendpredictionofagriculturalfuturespriceusingimageencodingandattentionmechanisms AT zehuiyu improvingtrendpredictionofagriculturalfuturespriceusingimageencodingandattentionmechanisms |