IDEAS home Printed from https://ideas.repec.org/a/for/ijafaa/y2018i50p35-41.html
   My bibliography  Save this article

Deep Learning for Forecasting

Author

Listed:
  • Tim Januschowski
  • Jan Gasthaus
  • Yuyang Wang
  • Syama Sundar Rangapuram
  • Laurent Callot

Abstract

While the term "deep learning" (DL) has only been coined in the last few years, the techniques it refers to have been in development since the 1950s, namely artificial neural networks (NN or ANN for short). DL has scored major successes in image recognition, natural language processing (e.g. machine translation and speech recognition), and autonomous agents such as Google Deep Mind's AlphaGo. It is often used as a synonym for artificial intelligence (AI), by which name it has received extensive press coverage. This first of two installments of an article from Tim Januschowski and colleagues presents a tutorial on the basics of DL with illustrations of how it has been applied for forecasting Amazon product sales and other variables. The second installment will explore current trends and challenges in applying DL to forecasting problems. Copyright International Institute of Forecasters, 2018

Suggested Citation

  • Tim Januschowski & Jan Gasthaus & Yuyang Wang & Syama Sundar Rangapuram & Laurent Callot, 2018. "Deep Learning for Forecasting," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 50, pages 35-41, Summer.
    • Handle: RePEc:for:ijafaa:y:2018:i:50:p:35-41
    as

    Download full text from publisher

    File URL: https://foresight.forecasters.org/shop/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Ma, Shaohui & Fildes, Robert, 2022. "The performance of the global bottom-up approach in the M5 accuracy competition: A robustness check," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1492-1499.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Fildes, Robert & Kolassa, Stephan & Ma, Shaohui, 2022. "Post-script—Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1319-1324.
    4. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.

    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:for:ijafaa:y:2018:i:50:p:35-41. 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: Michael Gilliland (email available below). General contact details of provider: https://edirc.repec.org/data/iiforea.html .

    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.