Web: http://arxiv.org/abs/2004.10240

June 17, 2022, 1:12 a.m. | Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider,

stat.ML updates on arXiv.org arxiv.org

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 …

arxiv deep deep learning forecasting learning lg literature survey time time series time series forecasting tutorial

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