IDEAS home Printed from https://ideas.repec.org/a/spr/infsem/v18y2020i4d10.1007_s10257-019-00399-7.html
   My bibliography  Save this article

RETRACTED ARTICLE: Research on sales information prediction system of e-commerce enterprises based on time series model

Author

Listed:
  • Jian Liu

    (Changzhou Vocational Institute of Engineering)

  • Chunlin Liu

    (Nanjing University Business School)

  • Lanping Zhang

    (Changzhou Vocational Institute of Engineering)

  • Yi Xu

    (Changzhou Tiansheng New Materials Co, Ltd)

Abstract

Sales forecasting plays an important role in guiding the sales and marketing of e-commerce enterprises, and warehousing department planning warehouse location. At the same time, sales data can better reflect future sales trends. This paper establishes a sales forecasting and analysis model for commodities with common characteristics using their historical sales data through time series model, and forecasts the sales inventory of a certain kind of products from a quantitative point of view. In order to improve the predictive reliability, this paper introduces external observable data and qualitative analysis of historical data prediction model by using hidden Markov model to predict the characteristics of hidden values, so as to further improve the reliability of prediction model.

Suggested Citation

  • Jian Liu & Chunlin Liu & Lanping Zhang & Yi Xu, 2020. "RETRACTED ARTICLE: Research on sales information prediction system of e-commerce enterprises based on time series model," Information Systems and e-Business Management, Springer, vol. 18(4), pages 823-836, December.
    • Handle: RePEc:spr:infsem:v:18:y:2020:i:4:d:10.1007_s10257-019-00399-7
    DOI: 10.1007/s10257-019-00399-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10257-019-00399-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10257-019-00399-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    2. Antonis A Michis, 2015. "A wavelet smoothing method to improve conditional sales forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(5), pages 832-844, May.
    3. Arunraj, Nari Sivanandam & Ahrens, Diane, 2015. "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 321-335.
    4. Fan, Zhi-Ping & Che, Yu-Jie & Chen, Zhen-Yu, 2017. "Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis," Journal of Business Research, Elsevier, vol. 74(C), pages 90-100.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mingwei Sun & Katarzyna Grondys & Nazim Hajiyev & Pavel Zhukov, 2021. "Improving the E-Commerce Business Model in a Sustainable Environment," Sustainability, MDPI, vol. 13(22), pages 1-22, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    2. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    3. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    4. Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping, 2022. "Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1005-1024.
    5. Huang, Shupeng & Potter, Andrew & Eyers, Daniel & Li, Qinyun, 2021. "The influence of online review adoption on the profitability of capacitated supply chains," Omega, Elsevier, vol. 105(C).
    6. Li, Hengyun & Hu, Mingming & Li, Gang, 2020. "Forecasting tourism demand with multisource big data," Annals of Tourism Research, Elsevier, vol. 83(C).
    7. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2022. "Classification-based model selection in retail demand forecasting," International Journal of Forecasting, Elsevier, vol. 38(1), pages 209-223.
    8. Aaryan Gupta & Vinya Dengre & Hamza Abubakar Kheruwala & Manan Shah, 2020. "Comprehensive review of text-mining applications in finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    9. Zhang, Tianyu & Dong, Peiwu & Zeng, Yongchao & Ju, Yanbing, 2022. "Analyzing the diffusion of competitive smart wearable devices: An agent-based multi-dimensional relative agreement model," Journal of Business Research, Elsevier, vol. 139(C), pages 90-105.
    10. Praveen Ranjan Srivastava & Prajwal Eachempati & Ajay Kumar & Ashish Kumar Jha & Lalitha Dhamotharan, 2023. "Best strategy to win a match: an analytical approach using hybrid machine learning-clustering-association rule framework," Annals of Operations Research, Springer, vol. 325(1), pages 319-361, June.
    11. Bag, Sujoy & Tiwari, Manoj Kumar & Chan, Felix T.S., 2019. "Predicting the consumer's purchase intention of durable goods: An attribute-level analysis," Journal of Business Research, Elsevier, vol. 94(C), pages 408-419.
    12. Bi-Huei Tsai & Yao-Min Huang, 2023. "Comparing the Substitution of Nuclear Energy or Renewable Energy for Fossil Fuels between the United States and Africa," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    13. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    14. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    15. Radu Lixăndroiu, 2017. "E-Commerce Trend Forecasting For Romania Vs European Union," Journal of Smart Economic Growth, , vol. 2(1), pages 98-108, March.
    16. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    17. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    18. Symitsi, Efthymia & Stamolampros, Panagiotis & Daskalakis, George & Korfiatis, Nikolaos, 2021. "The informational value of employee online reviews," European Journal of Operational Research, Elsevier, vol. 288(2), pages 605-619.
    19. Egor V. Dudukalov & Galymzhan O. Spabekov & Liudmila V. Kashirskaya & Andrei V. Sevbitov & Olga Yurievna Voronkova & Lidia Vasyutkina, 2020. "Fiscal goals of regulating the activities of the institute of controlled foreign companies in the digital economy," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 972-983, December.
    20. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infsem:v:18:y:2020:i:4:d:10.1007_s10257-019-00399-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.