摘要
提出一种基于自回归求和移动平均(ARIMA)与人工神经网络(ANN)的区间时间序列混合模型,并用混合模型分别对区间中值序列和区间半径序列建模.采用Monte Carlo方法生成模拟区间序列,分别用ARIMA、ANN和混合模型3种方法进行建模和预测实验,并用统计学方法检验模型误差.最后分别采用3种方法对H市轨道交通某号线牵引能耗区间序列进行了建模和预测,实验结果表明混合模型的建模精度和预测性能均优于单一模型.
A hybrid model based on the autoregressive integrated moving average（ARIMA） model and the artificial neural network（ANN） model is proposed to model and predict interval-value time series. The interval-valued time series are converted to the mid-point and the half-range series, the forecasting of which is accomplished through a hybrid model, respectively. The evaluation of the ARIMA, ANN and hybrid models is based on the estimation of the average behavior of the mean squared error with synthetic and real interval-valued series in the framework of a Monte Carlo experiment. The experimental results show that the hybrid model is an effective way to improve the forecasting accuracy achieved by any one of the models separately.
出处
《控制与决策》
EI
CSCD
北大核心
2013年第12期1915-1920,共6页
Control and Decision
作者简介
岳继光（1961-），男，教授，博士生导师，从事过程控制、计算机控制等研究；
杨臻明（1982-），男，博士生，从事非线性系统、神经元网络的研究．
