[2004.10240] Neural forecasting: Introduction and literature overviewopen searchopen navigation menucontact arXivarXiv Twitter

Neural network based forecasting methods have become ubiquitous in large-scale industrial forecasting applications over the last years. As the prevalence of neural network based solutions among the best entries in the recent M4 competition shows, the recent popularity of neural forecasting methods is not limited to industry and has also reached academia. This article aims at providing an introduction and an overview of some of the advances that have permitted the resurgence of neural networks in machine learning. Building on these foundations, the article then gives an overview of the recent literature on neural networks for forecasting and applications.

6 mentions: @ml_review@spyrosmakrid@LoVVgE@macroarb
Date: 2020/04/23 21:51

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@ml_review Neural forecasting: Introduction and Literature Overview [66pp] By Amazon Research @LoVVgE @canerturkmen @lostella Concise Deep Learning introduction through the prism of Forecasting. t.co/Kq6PIj5TNe t.co/2R3TK2EeKz
@LoVVgE We posted a preprint of our review article on probabilistic time series forecasting with neural networks (t.co/JJiIDBMZFv). Kudos to all my amazing collaborators (@confusionmatrix @canerturkmen @lostella, et al.), in particular Konstantinos for spearheading the endeavor! t.co/ptgoB3ZM6Q
@spyrosmakrid An excellent paper t.co/jZH6MsK7Ua "Neural forecasting: Introduction and literature overview" by Benidis et al. (Amazon DE AI) discusses neural forecast methods and their applications. They are invited to the M5 Competition in #Kaggle to prove their claim with real data.

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