Forecasting of commercial sales with large scale gaussian processes

Rodrigo Rivera, Evgeny Burnaev

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

    10 Citations (Scopus)

    Abstract

    This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decision-making tool for management.

    Original languageEnglish
    Title of host publicationProceeding - 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
    EditorsRaju Gottumukkala, George Karypis, Vijay Raghavan, Xindong Wu, Lucio Miele, Srinivas Aluru, Xia Ning, Guozhu Dong
    PublisherIEEE Computer Society
    Pages625-634
    Number of pages10
    ISBN (Electronic)9781538614808
    DOIs
    Publication statusPublished - 15 Dec 2017
    Event17th IEEE International Conference on Data Mining Workshops, ICDMW 2017 - New Orleans, United States
    Duration: 18 Nov 201721 Nov 2017

    Publication series

    NameIEEE International Conference on Data Mining Workshops, ICDMW
    Volume2017-November
    ISSN (Print)2375-9232
    ISSN (Electronic)2375-9259

    Conference

    Conference17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
    Country/TerritoryUnited States
    CityNew Orleans
    Period18/11/1721/11/17

    Keywords

    • Demand forecasting
    • Fast moving consumer goods
    • Gaussian Processes
    • Retail

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