农产品销量预测的支持向量机方法

运用支持向量机(Support Vector Machine,SVM)智能预测方法对农产品的消费市场需求进行动态预测。为提高农产品销量预测精度,充分考虑了农产品供需随天气变化、气候条件、节假日等因素的影响而动态变化的情况,将这些影响因素纳入农产品销量预测中,运用模糊理论进行模糊化处理;在此基础上提出以支持向量机方法为主、多方法融合为辅的智能预测系统,对农产品销量进行动态预测。实际算例验证了这一智能预测系统的精确性。

A Support Vector Machine Method for Sales Forecast of Farm Products

A dynamic forecasting system of demands for farm products in consuming market is studied by means of applying the intelligent forecast method SVM(Support Vector Machine).In order to achieve higher forecast precision of farm product sale quantities,such factors as weather,climate conditions and demands on holidays etc.,are introduced to the forecasting model,and the Fuzzy Theory is also applied to cope with the problem of fuzzification.Furthermore,an intelligent forecasting system is constructed,which is mainly based on SVM theory and combined with many other techniques.Using this system to dynamically forecast the demands for farm products,the practical application shows the precision of the forecasting method.