mlsanityData to 7 Oct 2026

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

arXiv 1703.07015 ↗First posted 21 Mar 2017cs.LGcited by 869

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Cited by 31 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.

Key facts. Cited by 869 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 81; 2022: 96; 2023: 120; 2024: 192; 2025: 220; 2026: 160.

869 papers.

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  1. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  2. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  3. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  4. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  5. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  6. 2024Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusioncited by 634
  7. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  8. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  9. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 577
  10. 2021TS2Vec: Towards Universal Representation of Time Seriescited by 463
  11. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
  12. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
38 more
  1. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 335
  2. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 333
  3. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 332
  4. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 298
  5. 2021Monash Time Series Forecasting Archivecited by 256
  6. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 242
  7. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 195
  8. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  9. 2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 170
  10. 2022SAITS: Self-Attention-based Imputation for Time Seriescited by 167
  11. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 163
  12. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 158
  13. 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 149
  14. 2022FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecastingcited by 147
  15. 2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
  16. 2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 146
  17. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  18. 2022CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecastingcited by 143
  19. 2023UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecastingcited by 139
  20. 2023Meta-Transformer: A Unified Framework for Multimodal Learningcited by 136
  21. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasterscited by 127
  22. 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 126
  23. 2021AdaRNN: Adaptive Learning and Forecasting of Time Seriescited by 120
  24. 2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 113
  25. 2024TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysiscited by 113
  26. 2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictorscited by 110
  27. 2024Is Mamba Effective for Time Series Forecasting?cited by 110
  28. 2023ForecastPFN: Synthetically-Trained Zero-Shot Forecastingcited by 102
  29. 2024CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patternscited by 102
  30. 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 100
  31. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  32. 2024UniTS: A Unified Multi-Task Time Series Modelcited by 93
  33. 2024From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflectioncited by 93
  34. 2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 89
  35. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  36. 2024Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Expertscited by 88
  37. 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecastingcited by 86
  38. 2024Timer-XL: Long-Context Transformers for Unified Time Series Forecastingcited by 83

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