Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches. Consequently, over the last years, these methods are now ubiquitous in large-scale industrial forecasting applications and hav...

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Bibliographic Details
Published in:ACM computing surveys 2023-06, Vol.55 (6), p.1-36, Article 121
Main Authors: Benidis, Konstantinos, Rangapuram, Syama Sundar, Flunkert, Valentin, Wang, Yuyang, Maddix, Danielle, Turkmen, Caner, Gasthaus, Jan, Bohlke-Schneider, Michael, Salinas, David, Stella, Lorenzo, Aubet, François-Xavier, Callot, Laurent, Januschowski, Tim
Format: Article
Language:English
Subjects:
Artificial intelligence
Computer science
Computing methodologies
Deep learning
Forecasting
General and reference
Literature reviews
Mathematics of computing
Probabilistic algorithms
Probabilistic reasoning
Surveys and overviews
Time series
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Summary:Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches. Consequently, over the last years, these methods are now ubiquitous in large-scale industrial forecasting applications and have consistently ranked among the best entries in forecasting competitions (e.g., M4 and M5). This practical success has further increased the academic interest to understand and improve deep forecasting methods. In this article we provide an introduction and overview of the field: We present important building blocks for deep forecasting in some depth; using these building blocks, we then survey the breadth of the recent deep forecasting literature.
ISSN:0360-0300
1557-7341
DOI:10.1145/3533382