基于ARIMA和ANN联合模型的物流数量预测

Z. Jing, Zhu Jin-fu
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引用次数: 0

摘要

一些企业的物流量具有增长性和季节性波动的双重特征。多季节ARIMA模型具有线性拟合能力,而人工神经网络具有非线性关系映射能力。针对单一模型存在的缺陷,提出了一种基于多季节ARIMA模型和人工神经网络模型的组合预测模型,预测结果表明,该组合预测模型在许多性能方面都优于单一模型。组合预测模型为物流数量预测提供了一种新的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Logistics amount forecasting based on combined ARIMA and ANN model
The logistics amount of some enterprises has a dual characters of growth and seasonal fluctuation. Multiple seasonal ARIMA model has linear fitting ability and ANN has the ability of nonlinear relationship mapping. A combined forecasting model based on multiple seasonal ARIMA model and ANN model was proposed to overcome the defects of single model, and the prediction result shows that the combined forecasting model is superior to the single model in many performance aspects. Combined forecasting model offers a new effective method of logistics amount prediction.
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