Research on Sales Forecasting Based on ARIMA and BP Neural Network Combined Model

被引：1

作者：

Ji, Shenjia ^{[1
]}

Yu, Hongyan ^{[2
]}

Guo, Yinan ^{[2
]}

Zhang, Zongrun ^{[2
]}

机构：

[1] Australian Natl Univ, Coll Engn & Comp Sci, Canberra, ACT, Australia

[2] Shanghai Maritime Univ, Coll Transport & Commun, Shanghai, Peoples R China

来源：

PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION PROCESSING (ICIIP'16) | 2016年

基金：

中国国家自然科学基金;

关键词：

ARIMA Model; BP Neural Network Model; Composition Model; Prediction;

D O I：

10.1145/3028842.3028883

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A single ARIMA model cannot meet higher standards of prediction accuracy. Moreover, it can only deal with small prediction periods in the forecasting work. For the sake of prediction accuracy, we combined an ARIMA model with BP neural network. Firstly, an ARIMA forecasting model is established. Secondly the BP neural network is used to improve the single ARIMA model. The residual of ARIMA model is trained and fitted by BP neural network. Finally, more accurate results are given through combination with the forecast results of ARIMA model. The practice turns out that, compared with single ARIMA model, the prediction accuracy of new ARIMA model improved by BP neural networks is obviously enhanced, with an average error of forecast decreasing 10.4% by a large margin. Thus, the combined model proposed by this paper can be used in future prediction researches and industrial data analysis.

引用

页数：6

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