IDEAS home Printed from https://ideas.repec.org/a/abx/journl/y2019id174.html
   My bibliography  Save this article

Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting

Author

Listed:
  • S. V. Sholtanyuk

Abstract

Applicability of neural nets in time series forecasting has been considered and researched. For this, training of neural network on various time series with preliminary selection of optimal hyperparameters has been performed. Comparative analysis of received neural networking forecasting model with linear regression has been performed. Conditions, affecting on accuracy and stability of results of the neural network, have been revealed.

Suggested Citation

  • S. V. Sholtanyuk, 2019. "Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting," Digital Transformation, Educational Establishment “Belarusian State University of Informatics and Radioelectronicsâ€, issue 2.
  • Handle: RePEc:abx:journl:y:2019:id:174
    DOI: 10.38086/2522-9613-2019-2-60-68
    as

    Download full text from publisher

    File URL: https://dt.bsuir.by/jour/article/viewFile/174/101
    Download Restriction: no

    File URL: https://libkey.io/10.38086/2522-9613-2019-2-60-68?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

    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:abx:journl:y:2019:id:174. 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: Ð ÐµÐ´Ð°ÐºÑ†Ð¸Ñ (email available below). General contact details of provider: .

    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.