mlsanityData to 7 Oct 2026

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

arXiv 2004.10240 ↗First posted 21 Apr 2020cs.LG stat.MLcited by 117

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Abstract (arXiv)

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.

Key facts. Cited by 117 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 28; 2024: 23; 2025: 31; 2026: 27.

117 papers.

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  1. 2022Transformers in Time Series: A Surveycited by 489
  2. 2023TimeGPT-1cited by 149
  3. 2024Is Mamba Effective for Time Series Forecasting?cited by 110
  4. 2023Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecastingcited by 102
  5. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  6. 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecastingcited by 86
  7. 2023A Survey on Deep Learning based Time Series Analysis with Frequency Transformationcited by 40
  8. 2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
  9. 2023AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecastingcited by 36
  10. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  11. 2023DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalizationcited by 24
  12. 2023Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domaincited by 23
38 more
  1. 2021Flexible Model Aggregation for Quantile Regressioncited by 22
  2. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  3. 2023Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolutioncited by 19
  4. 2023Graph-based Time Series Clustering for End-to-End Hierarchical Forecastingcited by 16
  5. 2025DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignmentcited by 13
  6. 2023Deep Learning based Forecasting: a case study from the online fashion industrycited by 12
  7. 2021ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecastingcited by 11
  8. 2024Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equationscited by 11
  9. 2025Relational Conformal Prediction for Correlated Time Seriescited by 11
  10. 2025BEDTime: A Unified Benchmark for Automatically Describing Time Seriescited by 11
  11. 2025AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecastingcited by 11
  12. 2025Deep Learning for Time Series Forecasting: A Surveycited by 10
  13. 2025TIMING: Temporality-Aware Integrated Gradients for Time Series Explanationcited by 9
  14. 2023Machine Learning for Urban Air Quality Analytics: A Surveycited by 8
  15. 2024HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecastingcited by 8
  16. 2025End-to-End Probabilistic Framework for Learning with Hard Constraintscited by 8
  17. 2024Right on Time: Revising Time Series Models by Constraining their Explanationscited by 7
  18. 2025ms-Mamba: Multi-scale Mamba for Time-Series Forecastingcited by 7
  19. 2021Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logscited by 6
  20. 2024Multivariate Probabilistic Time Series Forecasting with Correlated Errorscited by 6
  21. 2024MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMscited by 6
  22. 2024Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Surveycited by 6
  23. 2023Better Batch for Deep Probabilistic Time Series Forecastingcited by 5
  24. 2024A Survey of Transformer Enabled Time Series Synthesiscited by 5
  25. 2024MVG-CRPS: A Robust Loss Function for Multivariate Probabilistic Forecastingcited by 5
  26. 2023Hierarchical Forecasting at Scalecited by 4
  27. 2024System Safety Monitoring of Learned Components Using Temporal Metric Forecastingcited by 4
  28. 2025Conformal Prediction for Time-series Forecasting with Change Pointscited by 4
  29. 2026Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemicscited by 4
  30. 2022A Model-Constrained Tangent Slope Learning Approach for Dynamical Systemscited by 3
  31. 2023FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecastingcited by 3
  32. 2024From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learningcited by 3
  33. 2024Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecastingcited by 3
  34. 2024Strada-LLM: Graph LLM for traffic predictioncited by 3
  35. 2025ResCP: Reservoir Conformal Prediction for Time Series Forecastingcited by 3
  36. 2022Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Tradingcited by 2
  37. 2023A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areascited by 2
  38. 2023The Bayesian Context Trees State Space Model for time series modelling and forecastingcited by 2

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