综合分析了影响汽柴油消费需求的关键因素,并针对其具有自相关性、复杂性、数据量大等特点,采用主成分分析法对样本数据进行降维处理,形成新的样本集. 对支持向量机预测模型进行改进,在其基础之上引入时序动态因子,将上年的汽柴油需求历史数据作为时序反馈因子引入模型,从而形成新的动态反馈拟合模型,建立相应的需求预测模型. 对1996~2012年的汽柴油需求预测进行实例研究,并将本文中所提方法的预测结果与灰色GM(1,1)模型、BP神经网络模型进行对比分析. 结果表明本文中的主成分分析与改进支持向量机预测方法相对于GM(1,1)模型其预测误差均值分别降低了72.7%和74.86%,相对于BP神经网络其预测误差均值分别降低了81.3%和81畅66%,从而证明了此方法的有效性和优越性.%Firstly, a comprehensive analysis of the key factors affecting consumer demand for gasoline and diesel is made for self-relevance, complexity and data volume, etc.A principal component analysis is made to reduce the dimension of the sample data, and a new set of samples is formed.Then, by improving the support vector machine model and introducing a dynamic factor in the timing of its foundation, and the demand for gasoline and diesel last year historical data into the model as the timing of the feedback factor, thus forming a new dynamic feedback fitting model, an appropriate demand forecasting model is estab-lished.Finally, a case study is made on forecasting demand for gasoline and diesel in the 1996~2012, and the proposed method of predicting and gray GM (1,1) model, and BP neural network model are ana-lyzed.The results show that the improved prediction method relative to the GM support vector machine principal component analysis (1,1) model of the prediction errors are respectively 72.7%, 74.86%low-er, and that comparing with the BP neural network, the prediction errors are reduced on average by 81. 3%,81.66%.Results show that the principal component analysis using improved support vector machine method is superior to existing methods, which proves the effectiveness and superiority of this method.
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