论文部分内容阅读
本文根据公交客运量数据的变化特征,提出了用不同模型分别描述其确定性增长项、周期性摆动项和随机性波动项的组合时序模型。该模型具有较高的拟合精度和良好的外推预测性能,可避免其它预测方法的局限性。应用实例表明,对1988年的预测精度高达99.8％,该法亦可用于其它社会经济系统的分析预测。
In this paper, according to the characteristics of the change of bus passenger volume data, a combined sequential model of different deterministic growth items, cyclical items and stochastic volatility items is proposed. The model has high fitting accuracy and good extrapolation prediction performance, which can avoid the limitations of other prediction methods. The application example shows that the prediction accuracy to 1988 is as high as 99.8%. This method can also be used for the analysis and prediction of other socio-economic systems.