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 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.
Top-30
Citations by journals
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Knowledge-Based Systems
3 publications, 5.17%
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Expert Systems with Applications
3 publications, 5.17%
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Energies
2 publications, 3.45%
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International Journal of Forecasting
2 publications, 3.45%
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Electronics (Switzerland)
2 publications, 3.45%
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Applied Intelligence
2 publications, 3.45%
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Palgrave Advances in the Economics of Innovation and Technology
2 publications, 3.45%
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Neural Computing and Applications
2 publications, 3.45%
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Designs
1 publication, 1.72%
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Physics and Chemistry of the Earth
1 publication, 1.72%
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Technological Forecasting and Social Change
1 publication, 1.72%
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Artificial Intelligence in Medicine
1 publication, 1.72%
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Information Fusion
1 publication, 1.72%
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Machine Learning with Applications
1 publication, 1.72%
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Atmosphere
1 publication, 1.72%
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Human-Centric Intelligent Systems
1 publication, 1.72%
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Renewable and Sustainable Energy Reviews
1 publication, 1.72%
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Future Generation Computer Systems
1 publication, 1.72%
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ACM Transactions on Knowledge Discovery from Data
1 publication, 1.72%
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Journal of Network and Systems Management
1 publication, 1.72%
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Energy and Buildings
1 publication, 1.72%
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Agronomy
1 publication, 1.72%
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Journal of Physics: Conference Series
1 publication, 1.72%
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Energy and AI
1 publication, 1.72%
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IEEE Access
1 publication, 1.72%
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Communications in Computer and Information Science
1 publication, 1.72%
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Computational Economics
1 publication, 1.72%
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IEEE Internet of Things Journal
1 publication, 1.72%
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IEEE Transactions on Signal Processing
1 publication, 1.72%
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Citations by publishers
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Elsevier
19 publications, 32.76%
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IEEE
17 publications, 29.31%
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Springer Nature
10 publications, 17.24%
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Multidisciplinary Digital Publishing Institute (MDPI)
7 publications, 12.07%
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Association for Computing Machinery (ACM)
3 publications, 5.17%
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IOP Publishing
1 publication, 1.72%
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- 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.