【摘要】 物流景气指数反映了一个国家物流业发展运行的总体情况,是衡量物流业活跃程度的关键指标,它与货运量、港口货物吞吐量、快递业务量等众多物流相关指标有着密切联系。对于物流景气指数的预测分析,有助于一国宏观经济政策的制定和实施。以物流景气指数为对象,使用GM(1,1)预测、BP神经网络预测和ARIMA预测三种模型对物流景气指数进行建模分析,通过三种模型的误差比较,结果发现ARIMA模型预测拟合度最佳,LPI的预测值通过二次差分转换后几乎落入到95%的置信区间内,真实值和预测值吻合度最高,很好地拟合了物流景气指数在时间序列上的变化趋势,最后以该模型预测分析2018年8月到12月的LPI数值。更多还原

【Abstract】 The logistics prosperity index could reflect the overall development and condition of the logistics industry of a country. It is a key indicator to measure the activity level of the logistics industry and is closely related to many logistics indicators such as freight volume,port cargo throughput and express delivery volume. The forecasting and analysis of the logistics prosperity index would help the formulation and implementation of the country’s macroeconomic policies. In this paper, the logistics prosperity index is modeled and analyzed using GM(1,1), BP neural network and ARIMA. Through the error comparison of the three, it is found that the ARIMA model has the best prediction-toreality fitness. At the end, this model is used to predict and analyze the LPI value of China between August and December 2018.更多还原