ACM Computing Surveys, volume 55, issue 6, pages 1-36

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

Konstantinos Benidis ¹

Syama Sundar Rangapuram ¹

Valentin Flunkert ¹

Yuyang Wang ²

Danielle Maddix ²

Caner Turkmen ¹

Jan Gasthaus ¹

Michael Bohlke-Schneider ¹

David Salinas ¹

Lorenzo Stella ¹

François-Xavier Aubet ¹

Laurent Callot ¹

Tim Januschowski ³

Hide authors affiliations Show authors affiliations: 3 affiliations

Amazon Research, Charlottenstrasse, Berlin, Germany |

Amazon Research, East Palo Alto, CA, USA |

Zalando SE, Berlin, Germany |

Publication type: Journal Article

Publication date: 2022-05-19

Association for Computing Machinery (ACM)

Journal: ACM Computing Surveys

Quartile SCImago

Quartile WOS

Impact factor: 16.6

ISSN: 03600300, 15577341

DOI: 10.1145/3533382

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Theoretical Computer Science

General Computer Science

Abstract

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.

By date By citations

Top-30

Citations by journals

	1 2 3
Knowledge-Based Systems	Knowledge-Based Systems, 3, 6% Knowledge-Based Systems 3 publications, 6%
International Journal of Forecasting	International Journal of Forecasting, 2, 4% International Journal of Forecasting 2 publications, 4%
Applied Intelligence	Applied Intelligence, 2, 4% Applied Intelligence 2 publications, 4%
Palgrave Advances in the Economics of Innovation and Technology	Palgrave Advances in the Economics of Innovation and Technology, 2, 4% Palgrave Advances in the Economics of Innovation and Technology 2 publications, 4%
Expert Systems with Applications	Expert Systems with Applications, 2, 4% Expert Systems with Applications 2 publications, 4%
Designs	Designs, 1, 2% Designs 1 publication, 2%
Energies	Energies, 1, 2% Energies 1 publication, 2%
Physics and Chemistry of the Earth	Physics and Chemistry of the Earth, 1, 2% Physics and Chemistry of the Earth 1 publication, 2%
Technological Forecasting and Social Change	Technological Forecasting and Social Change, 1, 2% Technological Forecasting and Social Change 1 publication, 2%
Artificial Intelligence in Medicine	Artificial Intelligence in Medicine, 1, 2% Artificial Intelligence in Medicine 1 publication, 2%
Information Fusion	Information Fusion, 1, 2% Information Fusion 1 publication, 2%
Electronics (Switzerland)	Electronics (Switzerland), 1, 2% Electronics (Switzerland) 1 publication, 2%
Machine Learning with Applications	Machine Learning with Applications, 1, 2% Machine Learning with Applications 1 publication, 2%
Atmosphere	Atmosphere, 1, 2% Atmosphere 1 publication, 2%
Human-Centric Intelligent Systems	Human-Centric Intelligent Systems, 1, 2% Human-Centric Intelligent Systems 1 publication, 2%
Renewable and Sustainable Energy Reviews	Renewable and Sustainable Energy Reviews, 1, 2% Renewable and Sustainable Energy Reviews 1 publication, 2%
Future Generation Computer Systems	Future Generation Computer Systems, 1, 2% Future Generation Computer Systems 1 publication, 2%
ACM Transactions on Knowledge Discovery from Data	ACM Transactions on Knowledge Discovery from Data, 1, 2% ACM Transactions on Knowledge Discovery from Data 1 publication, 2%
Journal of Network and Systems Management	Journal of Network and Systems Management, 1, 2% Journal of Network and Systems Management 1 publication, 2%
Energy and Buildings	Energy and Buildings, 1, 2% Energy and Buildings 1 publication, 2%
Agronomy	Agronomy, 1, 2% Agronomy 1 publication, 2%
Journal of Physics: Conference Series	Journal of Physics: Conference Series, 1, 2% Journal of Physics: Conference Series 1 publication, 2%
Energy and AI	Energy and AI, 1, 2% Energy and AI 1 publication, 2%
IEEE Access	IEEE Access, 1, 2% IEEE Access 1 publication, 2%
Communications in Computer and Information Science	Communications in Computer and Information Science, 1, 2% Communications in Computer and Information Science 1 publication, 2%
Neural Computing and Applications	Neural Computing and Applications, 1, 2% Neural Computing and Applications 1 publication, 2%
Computational Economics	Computational Economics, 1, 2% Computational Economics 1 publication, 2%
IEEE Internet of Things Journal	IEEE Internet of Things Journal, 1, 2% IEEE Internet of Things Journal 1 publication, 2%
IEEE Transactions on Signal Processing	IEEE Transactions on Signal Processing, 1, 2% IEEE Transactions on Signal Processing 1 publication, 2%
	1 2 3

Citations by publishers

	2 4 6 8 10 12 14 16 18
Elsevier	Elsevier, 17, 34% Elsevier 17 publications, 34%
IEEE	IEEE, 14, 28% IEEE 14 publications, 28%
Springer Nature	Springer Nature, 9, 18% Springer Nature 9 publications, 18%
Multidisciplinary Digital Publishing Institute (MDPI)	Multidisciplinary Digital Publishing Institute (MDPI), 5, 10% Multidisciplinary Digital Publishing Institute (MDPI) 5 publications, 10%
Association for Computing Machinery (ACM)	Association for Computing Machinery (ACM), 3, 6% Association for Computing Machinery (ACM) 3 publications, 6%
IOP Publishing	IOP Publishing, 1, 2% IOP Publishing 1 publication, 2%
	2 4 6 8 10 12 14 16 18

We do not take into account publications without a DOI.
Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
Statistics recalculated weekly.

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Benidis K. et al. Deep Learning for Time Series Forecasting: Tutorial and Literature Survey // ACM Computing Surveys. 2022. Vol. 55. No. 6. pp. 1-36.

GOST all authors (up to 50) Copy

Benidis K., Rangapuram S. S., Flunkert V., Wang Y., Maddix D., Turkmen C., Gasthaus J., Bohlke-Schneider M., Salinas D., Stella L., Aubet F., Callot L., Januschowski T. Deep Learning for Time Series Forecasting: Tutorial and Literature Survey // ACM Computing Surveys. 2022. Vol. 55. No. 6. pp. 1-36.

RIS |

Cite this

RIS Copy

TY - JOUR

DO - 10.1145/3533382

UR - https://doi.org/10.1145/3533382

TI - Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

T2 - ACM Computing Surveys

AU - Benidis, Konstantinos

AU - Rangapuram, Syama Sundar

AU - Flunkert, Valentin

AU - Wang, Yuyang

AU - Maddix, Danielle

AU - Turkmen, Caner

AU - Gasthaus, Jan

AU - Bohlke-Schneider, Michael

AU - Salinas, David

AU - Stella, Lorenzo

AU - Aubet, François-Xavier

AU - Callot, Laurent

AU - Januschowski, Tim

PY - 2022

DA - 2022/05/19 00:00:00

PB - Association for Computing Machinery (ACM)

SP - 1-36

IS - 6

VL - 55

SN - 0360-0300

SN - 1557-7341

ER -

BibTex |

Cite this

BibTex Copy

@article{2022_Benidis,

author = {Konstantinos Benidis and Syama Sundar Rangapuram and Valentin Flunkert and Yuyang Wang and Danielle Maddix and Caner Turkmen and Jan Gasthaus and Michael Bohlke-Schneider and David Salinas and Lorenzo Stella and François-Xavier Aubet and Laurent Callot and Tim Januschowski},

title = {Deep Learning for Time Series Forecasting: Tutorial and Literature Survey},

journal = {ACM Computing Surveys},

year = {2022},

volume = {55},

publisher = {Association for Computing Machinery (ACM)},

month = {may},

url = {https://doi.org/10.1145/3533382},

number = {6},

pages = {1--36},

doi = {10.1145/3533382}

}

MLA

Cite this

MLA Copy

Benidis, Konstantinos, et al. “Deep Learning for Time Series Forecasting: Tutorial and Literature Survey.” ACM Computing Surveys, vol. 55, no. 6, May. 2022, pp. 1-36. https://doi.org/10.1145/3533382.

Found error?

Publisher

Association for Computing Machinery (ACM)

Journal

ACM Computing Surveys

Quartile SCImago

Quartile WOS

Impact factor

16.6

ISSN

03600300 (Print)
15577341 (Electronic)