Computer Science > Machine Learning
[Submitted on 21 Apr 2020 (v1), last revised 15 Jun 2022 (this version, v2)]
Title:Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
View PDFAbstract: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.
Submission history
From: Konstantinos Benidis [view email][v1] Tue, 21 Apr 2020 18:53:42 UTC (165 KB)
[v2] Wed, 15 Jun 2022 20:16:03 UTC (362 KB)
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