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Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

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arxiv 2004.10240 v2 pith:5SUUKVC5 submitted 2020-04-21 cs.LG stat.ML

Konstantinos Benidis , Syama Sundar Rangapuram , Valentin Flunkert , Yuyang Wang , Danielle Maddix , Caner Turkmen , Jan Gasthaus , Michael Bohlke-Schneider
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David Salinas Lorenzo Stella Francois-Xavier Aubet Laurent Callot Tim Januschowski
This is my paper · ORCID
classification cs.LGstat.ML
keywords forecastingdeepmethodsapplicationsblocksbuildinglearningliterature
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

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  1. Probabilistic Residual Learning for Online Recommendations

    cs.IR 2026-07 conditional novelty 6.0 of 10

    PRL adds a cluster-aware, causality-adjusted residual correction layer to any base recommender, improving cold-start cross-domain recommendation accuracy in experiments.

  2. Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

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    TimeMCL uses Winner-Takes-All training of multiple heads to quantize the conditional distribution of future time series, producing diverse forecasts at low inference cost.

  3. A Novel Short-Term Anomaly Prediction for IIoT with Software Defined Twin Network

    cs.NI 2025-09 reject novelty 4.0 of 10

    A T+15 second anomaly prediction framework for IIoT, built on SDN telemetry and a digital twin, with reported LightGBM-GPU F2 score 0.822 on CICAPT-IIoT2024.

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