AR(p)与指数平滑组合预测算法

The Combined Predicton Approach of Auto-regressive and Exponential Smoothing

本文提出一种对铜锍品位进行预测的新方法 ,以采集的现场数据为基础 ,采用系统辨识动态地建立了 AR( p)模型与三次指数平滑模型。 AR( p)模型要求数据对象是平稳时间序列 ,而三次指数平滑模型的数据对象具有随机性 ,考虑到铜锍品位的波动性 ,本文将二模型按最小二乘法原理 ,以组合预测误差平方和为目标函数 ,通过使误差平方和极小化来确定两种预测方法的优化 ,建立了一种新的组合模型 。

This paper came up with a new method for forecasting the grade of copper matte based on the collected data from the factory and it established the dynamic Auto-Regressive and Exponential Smooth model by the system identification.The Data target of the Auto-Regressive model requires time series is smooth,however,the data target of Exponential Smoothing is random.The grade fluctuation of copper matte being considered,this paper established a new kind of combined model,in which the forecasting error could get the minimum among three models.The square of the forecasting error was regarded as the target function and the best weight numbers were gotten according to the principle of the minimal least square.