IDEAS home Printed from https://ideas.repec.org/a/vrs/poicbe/v19y2025i1p2162-2177n1016.html
   My bibliography  Save this article

Forecasting Television Audience Trends for Romanian News Channels Using the ARIMA Model

Author

Listed:
  • Săseanu Ramona

    (University of Craiova, Craiova, Romania)

  • Strîmbeanu Ana-Maria

    (Bucharest University of Economic Studies, Bucharest, Romania)

  • Barbu Alexandra

    (Bucharest University of Economic Studies, Bucharest, Romania)

Abstract

This research seeks to provide a thorough analysis of the broadcast reach of the leading news programs from five major television stations in Romania: TVR, PRO TV, Antena 1, Kanal D, and Prima TV, spanning the years 2018 to 2022. News journals are essential in educating the public and shaping opinions, making it essential to comprehend the behavior and preferences of viewers. By examining past data and applying prediction techniques, the study aims to uncover trends and patterns in audience dynamics. Using the ARIMA model, the research predicts audience performance and compares the forecast with actual data from 2023, evaluating the accuracy. The results offer valuable insights for those involved in the television industry, such as media professionals and stakeholders who can strategically plan and develop content in a way that is beneficial to their organization and to the wider public. As a result of the deeper understanding of audience scope, the study establishes a robust framework for future media analytics studies.

Suggested Citation

  • Săseanu Ramona & Strîmbeanu Ana-Maria & Barbu Alexandra, 2025. "Forecasting Television Audience Trends for Romanian News Channels Using the ARIMA Model," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 19(1), pages 2162-2177.
  • Handle: RePEc:vrs:poicbe:v:19:y:2025:i:1:p:2162-2177:n:1016
    DOI: 10.2478/picbe-2025-0168
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/picbe-2025-0168
    Download Restriction: no

    File URL: https://libkey.io/10.2478/picbe-2025-0168?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
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:vrs:poicbe:v:19:y:2025:i:1:p:2162-2177:n:1016. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.