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货运量预测是制定物流政策和决定物流基础设施布局的重要依据。针对受多因素影响的货运量预测具备较强非线性和模糊性特征，提出一种基于趋势分解和小波变换的多重“分解—集成”预测方法。利用趋势分解将货运量分解为趋势项和非趋势项，通过小波分解将非趋势项进一步分解成低频项和高频项，分别建立预测模型，选用相加集成得到货运量预测值。实证表明，“分解—集成”的预测策略将非平稳货运量分解为相对平稳的子序列组合，降低了问题复杂度，有效提高了预测性能，与传统的趋势分解预测模型和小波分解预测模型相比，多重“分解—集成”预测模型精度更高。

Logistics Freight Volume Forecasting Based on Multilevel Decompose-ensemble Method

Logistics freight volume forecasting is essential for forming logistics policy and determining the logistics infrastructure layout, which reflects strong- nonlinearity and ambiguity due to various affecting factors. A new forecasting approach based on multilevel decompose-ensemble is proposed for logistics freight volume. Original freight volume is firstly decomposed into trend component and non- trend component in accordance with trend decomposition. Then, non-trend component is further decomposed into a low frequency subseries and a several high frequency subseries by using of wavelet decomposition. With respect to their different features, trend component, low frequency non-trend component and high frequency non-trend component are respective forecasted. The prediction result of freight volume is the superimposition of these subseries predictions. Non-stationary time series is resolved into relatively stationary subsequences in accordance with trend decomposition and wavelet decomposition. The empirical test proves that the proposed forecasting method based on multilevel decompose-ensemble method is higher accuracy, which is compared with traditional decompose-ensemble forecasting method based on trend decomposition or wavelet decomposition.