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About this paper
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider and 5 more
The authors present a tutorial and literature survey of deep time-series forecasting, explaining how architecture, input processing, and output or loss choices shape forecasting systems. They review method-selection guidance and competition evidence, including ES-RNN’s M4 win and deep methods’ second- and third-place M5 finishes. The survey finds no clearly superior architecture, and notes neural networks may not suit every task.
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