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28 July 2023 A prediction method of grain yield based on SES-SVR residual correction
Haifeng Xu, Guoliang Wang, Peng Zhang
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Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 1275622 (2023) https://doi.org/10.1117/12.2685911
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China
Abstract
China is a large population country,whose food security issue is of great importance to people's livelihood. Therefore analyzing and predicting grain output is significant in China. In view of the second exponential smoothing model(SES) and Support Vector Regression algorithm(SVR), it proposes SES-SVR residual correction model to predict grain yield. It not only highlights the advantages of the trend of time series, but also increases the robustness of the sequence. For the grain yield data of Yunnan Province, the SES-SVR residual correction model has smaller error and higher prediction accuracy, which has certain guiding significance for grain yield data prediction.
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Haifeng Xu, Guoliang Wang, and Peng Zhang "A prediction method of grain yield based on SES-SVR residual correction", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 1275622 (28 July 2023); https://doi.org/10.1117/12.2685911
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KEYWORDS
Data modeling
Statistical modeling
Mathematical optimization
Support vector machines
Data processing
MATLAB
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