Two forecasting model selection methods based on time series image feature augmentation
Abstract Forecasting and early warning of agricultural product prices is a crucial task in stream data event analysis and agricultural data mining. Existing methods for forecasting agricultural product prices suffer from inefficient feature engineering and challenges in handling imbalanced sample da...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10072-4 |
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| author | Wentao Jiang Quan Wang Hongbo Li |
| author_facet | Wentao Jiang Quan Wang Hongbo Li |
| author_sort | Wentao Jiang |
| collection | DOAJ |
| description | Abstract Forecasting and early warning of agricultural product prices is a crucial task in stream data event analysis and agricultural data mining. Existing methods for forecasting agricultural product prices suffer from inefficient feature engineering and challenges in handling imbalanced sample data. To address these issues, we propose a novel predictive model selection approach based on time series image encoding. Specifically, we utilize Gramian Angular Fields (GAF), Markov Transition Fields (MTF), and Recurrence Plots (RP) to transform time series data into image representations. We then introduce an Information Fusion Feature Augmentation (IFFA) method to effectively combine these time series images, ensuring that all relevant event information is preserved. The combined time series images (TSCI) are subsequently fed into a Convolutional Neural Network (CNN) classifier for model selection. Furthermore, to accommodate the unique characteristics of the data, we incorporate Transfer Learning (TL) and S-Folder Cross Validation (S-FCV) to optimize the model selection process, thereby mitigating overfitting due to limited or imbalanced data. Experimental results demonstrate that the proposed IFFA-TSCI-CNN-SFCV method outperforms existing approaches in terms of both efficiency and accuracy. |
| format | Article |
| id | doaj-art-a7ec19e183e24d5399b4d5ea290f0c77 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a7ec19e183e24d5399b4d5ea290f0c772025-08-20T03:42:22ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-10072-4Two forecasting model selection methods based on time series image feature augmentationWentao Jiang0Quan Wang1Hongbo Li2School of Internet of Things Engineering, Wuxi UniversitySchool of Internet of Things Engineering, Wuxi UniversityCollege of Mathematics and Information Science, South China Agricultural UniversityAbstract Forecasting and early warning of agricultural product prices is a crucial task in stream data event analysis and agricultural data mining. Existing methods for forecasting agricultural product prices suffer from inefficient feature engineering and challenges in handling imbalanced sample data. To address these issues, we propose a novel predictive model selection approach based on time series image encoding. Specifically, we utilize Gramian Angular Fields (GAF), Markov Transition Fields (MTF), and Recurrence Plots (RP) to transform time series data into image representations. We then introduce an Information Fusion Feature Augmentation (IFFA) method to effectively combine these time series images, ensuring that all relevant event information is preserved. The combined time series images (TSCI) are subsequently fed into a Convolutional Neural Network (CNN) classifier for model selection. Furthermore, to accommodate the unique characteristics of the data, we incorporate Transfer Learning (TL) and S-Folder Cross Validation (S-FCV) to optimize the model selection process, thereby mitigating overfitting due to limited or imbalanced data. Experimental results demonstrate that the proposed IFFA-TSCI-CNN-SFCV method outperforms existing approaches in terms of both efficiency and accuracy.https://doi.org/10.1038/s41598-025-10072-4Time series encodingForecasting model selectionFeature augmentation |
| spellingShingle | Wentao Jiang Quan Wang Hongbo Li Two forecasting model selection methods based on time series image feature augmentation Scientific Reports Time series encoding Forecasting model selection Feature augmentation |
| title | Two forecasting model selection methods based on time series image feature augmentation |
| title_full | Two forecasting model selection methods based on time series image feature augmentation |
| title_fullStr | Two forecasting model selection methods based on time series image feature augmentation |
| title_full_unstemmed | Two forecasting model selection methods based on time series image feature augmentation |
| title_short | Two forecasting model selection methods based on time series image feature augmentation |
| title_sort | two forecasting model selection methods based on time series image feature augmentation |
| topic | Time series encoding Forecasting model selection Feature augmentation |
| url | https://doi.org/10.1038/s41598-025-10072-4 |
| work_keys_str_mv | AT wentaojiang twoforecastingmodelselectionmethodsbasedontimeseriesimagefeatureaugmentation AT quanwang twoforecastingmodelselectionmethodsbasedontimeseriesimagefeatureaugmentation AT hongboli twoforecastingmodelselectionmethodsbasedontimeseriesimagefeatureaugmentation |