产品需求量非平稳时序的ANN-ARMA预测模型

针对基于非平稳时序的产品需求量预测方法存在的问题,研究了人工神经网络(ANN)与自回归滑动平均(ARMA)模型的集成建模与预测方法. 产品需求量的非平稳时序可分解为确定项和随机项两个部分,用人工神经网络模型拟合确定项,以表示非平稳的变化趋势;用自回归滑动平均模型拟合随机项,以表示平稳的随机成分. 将两个模型的预测值之和作为产品需求量的优化预测值. 仿真结果表明,集成模型的预测精度高于单一的人工神经网络模型.

ANN-ARMA Model for Forecasting Product Consumption Based on Non-Stationary Time Series

A new model of integrating artificial neural network (ANN) with auto regressive moving average (ARMA) is studied to handle existing problems of forecasting methods of product consumption based on non-stationary time series. Because the non-stationary time series can be divided into the certain and stochastic parts, the ANN-ARMA model is proposed. The certain part that is fitted by the ANN model denotes their non-stationary trend, and the stochastic part that is fitted by the ARMA model denotes their stationary and random component. The sum of forecast values of the ANN model and the ARMA model is considered as the optimal forecast value of future product consumption. A simulation example indicates the forecast precision of the ANN-ARMA model to be superior to that of the ANN model.