基于季节自回归单整移动平均模型的电梯交通流递归预测方法

Recursive Forecasting Method for Elevator Traffic Flow Based on SARIMA

针对电梯交通流预测提出了一种基于季节自回归单整移动平均(SARIMA)模型的递归预测方法.通过离线分析,对电梯交通流利用时间序列分析得到初始的SARIMA模型,引入异常值检测对训练数据中的异常值进行修正,利用修正的序列得到电梯交通流SARIMA模型;在线预测时,将离线得到修正的SARIMA模型转化为状态空间形式,通过Kalman滤波实时调整状态向量,实现电梯交通流的实时在线预测.仿真表明该方法具有很好的预测性能,且运行时间短,满足实时性的要求.

A recursive method based on seasonal autoregressive integration moving average （ SARIMA ） model to predict the elevator traffic flow was proposed. By off-line analysis, an initial SARIMA model was developed from time series analysis. And an outlier detection was introduced to amend the sequence, from which the SARIMA model of the traffic flow was obtained. At on-line analysis, the off-line amended SARIMA model was first transformed into a state space model, and then by adjusting the parameters of this model with Kalman filter, the elevator traffic flow on-line prediction was achieved. The simulation results show that the proposed method has good prediction performance and short running time, satisfying the real-time requirements.