基于支持向量机模型的北京城镇农产品冷链物流需求预测

Forecast of Cold Chain Logistics Demand for Agricultural Products in Beijing Based on Support Vector Machine Model

农产品冷链物流需求系统具有非线性、历史数据少、影响因素众多等复杂特征,而支持向量机在解决小样本、非线性和高维模式识别问题等方面具有突出优势。引入支持向量机模型,以北京城镇为例,从农产品供给、社会经济、冷链发展、人文、物流需求规模五个角度构建其指标体系,对其2000-2014年的农产品冷链物流需求进行建模,进而对2015-2020年城镇农产品冷链物流需求量进行预测。结果表明,建立的模型对冷链物流需求与其影响因素的非线性关系方面有较高的精度和应用价值,能够为农产品冷链物流规划者及政府提供定量的决策依据。

The cold chain logistics demand system of urban agricultural products in Beijing has many complex characteristics,such as non-linearity, few historical data, and many influencing factors, while support vector machine has outstanding advantages in solving the problems of small samples, non-linearity and high-dimensional pattern recognition. Therefore, the support vector machine model was introduced to train the data of the cold chain logistics demand of agricultural products in Beijing from 2000 to 2014, and then the cold chain logistics demand of agricultural products in Beijing from 2015 to 2020 was forecasted. The results showed that the support vector machine model can effectively fit the complex trend of agricultural products cold chain logistics demand system in Beijing, which could provide quantitative decision for agricultural products cold chain logistics planners and the government.