| 姜百臣冯凯杰彭思喜.基于改进持支向量机的猪肉价格预测研究[J].广东农业科学,2018,45(12):158-164 |
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基于改进持支向量机的猪肉价格预测研究 |
| Research on pork price prediction based on improved support vector machine |
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| DOI：10.16768/j.issn.1004-874X.2018.12.026 |
| 中文关键词: 猪肉价格预测 支持向量机 遗传算法 集成经验模态分解；猪周期 |
| 英文关键词: pork price prediction support vector machine genetic algorithm integrated empirical mode
decomposition hog cycle |
| 基金项目:广东省自然科学基金（2017A030313425）；广州市科技计划项目（201806030008） |
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| 摘要点击次数: 1584 |
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| 中文摘要: |
| 针对近年频现“价高伤民，价贱伤农”的“猪周期”现象，尝试使用集成经验模态分解（EEMD）
方法挖掘出 “猪周期”的价格波动机制，并引入遗传算法（GA）改进支持向量机。研究结果发现，通过
EEMD 方法能较好地展示出“猪周期”的循环轨迹；通过对比常用的预测模型，发现基于 EEMD 的 GASVM 模型预测精测更高，是一种更具有科学性的价格预测工具。 |
| 英文摘要: |
| In view of the "hog cycle" phenomenon that frequently occurs in recent years, "high-price and highrisk-to-kill farmers", this paper attempts to use the integrated empirical modality method EEMD method to excavate
the "hog cycle" of pork price fluctuations as a predictive length criterion, and introduces The genetic algorithm (GA)
is used to optimize the performance parameters such as the penalty parameter C, kernel function g, and loss function
p of the support vector machine (SVM) to further optimize the prediction performance of the SVM. The results showed
that: Digging through the EEMD method can accurately dig out the "hog cycle" of pork prices; through the comparison
of commonly used prediction models, the optimization performance of the support vector machine after optimization by
the genetic algorithm is optimal and robust enough. Sex, and more suitable for short "porcine cycle" predictions. The
GA-SVM model proposed in this paper helps to guard against the cyclical risks of pork price volatility and is a more
scientific price forecasting tool. |
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