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Deep Learning for Forecasting: Current Trends and Challenges

Author

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

Abstract

In the first installment of this two-part article, Tim Januschowski and colleagues presented a tutorial on the basics of Deep Learning (DL) through neural networks (NNs), with illustrations of how NNs have been applied for forecasting product sales and other variables at Amazon. In this segment, they describe the pros and cons of forecasting through NNs and discuss some areas of current research designed to improve the application of NNs for forecasting. Copyright International Institute of Forecasters, 2018

Suggested Citation

  • Tim Januschowski & Jan Gasthaus & Yuyang Wang & Syama Sundar Rangapuram & Laurent Callot, 2018. "Deep Learning for Forecasting: Current Trends and Challenges," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 51, pages 42-47, Fall.
    • Handle: RePEc:for:ijafaa:y:2018:i:51:p:42-47
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    Cited by:

    1. 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.
    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. 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.
    4. 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.

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