Research on sales forecasting based on ARIMA and BP neural network combined model

Shenjia Ji, Hongyan Yu, Yinan Guo, Zongrun Zhang

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    14 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings of the 2016 International Conference on Intelligent Information Processing, ICIIP 2016
    PublisherAssociation for Computing Machinery
    ISBN (Electronic)9781450347990
    DOIs
    Publication statusPublished - 23 Dec 2016
    Event2016 International Conference on Intelligent Information Processing, ICIIP 2016 - Wuhan, China
    Duration: 23 Dec 201625 Dec 2016

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference2016 International Conference on Intelligent Information Processing, ICIIP 2016
    Country/TerritoryChina
    CityWuhan
    Period23/12/1625/12/16

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