自适应动态三次指数平滑法在交通预测中的应用

针对传统三次指数平滑法预测模型中平滑系数固定不变、难以跟踪时间序列随时间变化的因素、无法反应不同时间段历史数据对预测结果的影响等缺点，提出了一种改进的动态三次指数平滑法.该方法通过误差平方、最小原则和地毯式搜索算法来获得动态调整的平滑因子，对波动范围较大且呈非线性变化规律的数据有很强的适应性.将自适应动态三次指数平滑法与SARIMA模型、三次指数平滑法进行对比，由铁路旅客发送量的仿真实例分析结果可知，自适应动态三次指数平滑法能更好地适应时间序列的变化趋势，具有良好的预测精度.

Application of Self-Adaptive and Dynamic Cubic ES Method Traffic Prediction

According to the defects that smoothing coefficient in traditional cubic ES prediction model is fixed, the traditional one cannot track time series due to the change over time, and can not reflect the influence of historical data on the prediction results over different time periods, an improved cubic ES method is presented in this paper, which is based on the principle of minimum sum of squared errors and blanket search algorithm to obtain the smothering factor of dynamic adjustment. It is greatly adaptable to the data with a large range of fluctuations and nonlinear regular change. Through the analysis on the simulation case of railway passenger traffic volume, the self-adaptive and dynamic cubic ES method can better adapt to the trend of time series with high prediction accuracy.