• Login
    View Item 
    •   MINDS@UW Home
    • MINDS@UW La Crosse
    • Murphy Library, UWL
    • UW-L Theses & Dissertations
    • View Item
    •   MINDS@UW Home
    • MINDS@UW La Crosse
    • Murphy Library, UWL
    • UW-L Theses & Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Applications of time series analysis for forecasting fuel sales and change point detection

    Thumbnail
    File(s)
    Corey_Calvin_Thesis.pdf (974.0Kb)
    Date
    2020-08
    Author
    Corey, Calvin J.
    Advisor(s)
    Reineke, David
    Metadata
    Show full item record
    Abstract
    This paper shows how time series models can be used to forecast motor vehicle fuel sales, and how to use those models to detect changes in the time series signals. The model used is a least squared regression model that considers seasonal trends, serial autocorrelation, day of week, holidays, and days since open. With these covariates, the models proved to be highly predictive with forecasts on gasoline and diesel fuel, as the maximum cumulative accuracy was at most 2.74% for gasoline and 1.33% for diesel fuel. It can also be shown that mean absolute error rates can be represented by a gamma distribution, and this can be used to detect changes in the time series signal.
    Subject
    Statistics
    Sales forecasting
    Fuel
    Permanent Link
    http://digital.library.wisc.edu/1793/81511
    Part of
    • UW-L Theses & Dissertations

    Contact Us | Send Feedback
     

     

    Browse

    All of MINDS@UWCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Contact Us | Send Feedback