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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
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    Cited by:

    1. 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.

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