Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
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.
Cite
@article{arxiv.2004.10240,
title = {Deep Learning for Time Series Forecasting: Tutorial and Literature Survey},
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 Francois-Xavier Aubet and Laurent Callot and Tim Januschowski},
journal= {arXiv preprint arXiv:2004.10240},
year = {2022}
}
Comments
33 pages, 6 figures