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Time Series Forecasting With Deep Learning: A Survey

arXiv 2004.13408 ↗First posted 28 Apr 2020stat.ML cs.LGcited by 457

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Abstract (arXiv)

Numerous deep learning architectures have been developed to accommodate the diversity of time series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time series forecasting -- describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, we outline some ways in which deep learning can also facilitate decision support with time series data.

Key facts. Cited by 457 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Apr 2020.

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Citing papers per year: 2021: 28; 2022: 52; 2023: 82; 2024: 101; 2025: 114; 2026: 80.

457 papers.

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  1. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  2. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  3. 2022Transformers in Time Series: A Surveycited by 489
  4. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 335
  5. 2023TSMixer: An All-MLP Architecture for Time Series Forecastingcited by 232
  6. 2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 170
  7. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  8. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasterscited by 127
  9. 2024Is Mamba Effective for Time Series Forecasting?cited by 110
  10. 2023ForecastPFN: Synthetically-Trained Zero-Shot Forecastingcited by 102
  11. 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 100
  12. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
38 more
  1. 2024Kolmogorov-Arnold Networks (KANs) for Time Series Analysiscited by 97
  2. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoningcited by 95
  3. 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecastingcited by 86
  4. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 86
  5. 2024SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusioncited by 75
  6. 2025Time-MQA: Time Series Multi-Task Question Answering with Context Enhancementcited by 74
  7. 2023OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensemblingcited by 71
  8. 2024Probabilistic Forecasting with Stochastic Interpolants and F"ollmer Processescited by 61
  9. 2024LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Promptingcited by 56
  10. 2023SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecastingcited by 53
  11. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  12. 2021Long-Range Transformers for Dynamic Spatiotemporal Forecastingcited by 46
  13. 2024Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Directioncited by 46
  14. 2024FilterNet: Harnessing Frequency Filters for Time Series Forecastingcited by 44
  15. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  16. 2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
  17. 2024FlowVQTalker: High-Quality Emotional Talking Face Generation through Normalizing Flow and Quantizationcited by 39
  18. 2022TACTiS: Transformer-Attentional Copulas for Time Seriescited by 38
  19. 2024From Similarity to Superiority: Channel Clustering for Time Series Forecastingcited by 37
  20. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  21. 2025LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arenacited by 36
  22. 2024Low-Rank Adaptation for Foundation Models: A Comprehensive Reviewcited by 35
  23. 2022Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modelingcited by 33
  24. 2024A Survey of Deep Learning and Foundation Models for Time Series Forecastingcited by 32
  25. 2024In-Context Fine-Tuning for Time-Series Foundation Modelscited by 31
  26. 2022WOODS: Benchmarks for Out-of-Distribution Generalization in Time Seriescited by 29
  27. 2023Toward a Foundation Model for Time Series Datacited by 28
  28. 2024Universal Time-Series Representation Learning: A Surveycited by 28
  29. 2024TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysiscited by 28
  30. 2023Model scale versus domain knowledge in statistical forecasting of chaotic systemscited by 27
  31. 2024Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learningcited by 26
  32. 2023TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Seriescited by 25
  33. 2022HyperTime: Implicit Neural Representation for Time Seriescited by 24
  34. 2026Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scalingcited by 24
  35. 2025Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecastingcited by 23
  36. 2022EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecastingcited by 22
  37. 2023PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformercited by 22
  38. 2021Creativity and Machine Learning: A Surveycited by 21

50 most cited of 457 All 457 (JSON)