survey

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

Authors:

Konstantinos Benidis,

Syama Sundar Rangapuram,

Valentin Flunkert,

Danielle Maddix,

Michael Bohlke-Schneider,

Lorenzo Stella,

François-Xavier Aubet,

Laurent Callot,

Tim JanuschowskiAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 6

Article No.: 121, Pages 1 - 36

https://doi.org/10.1145/3533382

Published: 07 December 2022 Publication History

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.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 6

June 2023

781 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3567471

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