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基于PCA和HMM的汽车保有量预测方法

, PP. 92-98

Keywords: 汽车保有量预测,隐马尔可夫模型,主成分分析,回归分析,灰色预测

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Abstract:

分析了常用的汽车保有量预测方法,提出了一种新的基于主成分分析和隐马尔可夫模型的汽车保有量预测方法。选取国民总收入、人均GDP、人口总数量、城市化率、固定资产投资总额、进出口总额、城镇居民人均可支配收入、钢材产量、公路货运量、公路客运量、社会消费品零售总额11个指标作为汽车保有量的主要影响因素,运用主成分分析提取了主要影响因素的主成分。以提取的主成分与汽车保有量分别作为自变量、因变量,建立了回归分析模型。以汽车保有量回归预测值的年增长率为隐状态,以回归预测值与实际值的相对误差为可见信号,建立了隐马尔科夫模型,并对的汽车保有量回归预测值进行修正。分析结果表明基于1994~2008年的中国汽车保有量及其主要影响因素的历史数据,应用提出的方法得到2009、2010年的汽车保有量修正值分别为6.22096×107、7.82512×107veh;与2009、2010年实际汽车保有量比较,相对误差分别为-0.95%、0.30%。可见,基于主成分分析和隐马尔科夫模型的汽车保有量预测方法具有良好的预测精度,能够适用于短期预测。

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