mlsanityData to 14 Oct 2026

A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

arXiv 1704.02971 ↗First posted 7 Apr 2017cs.LG stat.MLcited by 245

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Abstract (arXiv)

The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the relevant driving series to make predictions. In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. In the first stage, we introduce an input attention mechanism to adaptively extract relevant driving series (a.k.a., input features) at each time step by referring to the previous encoder hidden state. In the second stage, we use a temporal attention mechanism to select relevant encoder hidden states across all time steps. With this dual-stage attention scheme, our model can not only make predictions effectively, but can also be easily interpreted. Thorough empirical studies based upon the SML 2010 dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can outperform state-of-the-art methods for time series prediction.

Key facts. Cited by 245 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Apr 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 43; 2022: 46; 2023: 40; 2024: 40; 2025: 56; 2026: 20.

245 papers.

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  1. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  2. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  3. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 346
  4. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  5. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 197
  6. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 157
  7. 2022FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecastingcited by 147
  8. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  9. 2021AdaRNN: Adaptive Learning and Forecasting of Time Seriescited by 121
  10. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 110
  11. 2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 90
  12. 2023OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensemblingcited by 74
38 more
  1. 2022Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Versioncited by 59
  2. 2023The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challengescited by 59
  3. 2023Asynchrony-Robust Collaborative Perception via Bird's Eye View Flowcited by 58
  4. 2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 51
  5. 2021Long-Range Transformers for Dynamic Spatiotemporal Forecastingcited by 46
  6. 2021Do We Really Need Deep Learning Models for Time Series Forecasting?cited by 45
  7. 2021ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Modelscited by 43
  8. 2025R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimizationcited by 43
  9. 2024TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecastingcited by 40
  10. 2025Context-Alignment: Activating and Enhancing LLM Capabilities in Time Seriescited by 39
  11. 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loopcited by 39
  12. 2021RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecastingcited by 36
  13. 2022DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptationcited by 36
  14. 2024Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Modelscited by 36
  15. 2021Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecastingcited by 32
  16. 2021AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investmentcited by 32
  17. 2021Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transportcited by 32
  18. 2021Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detectioncited by 31
  19. 2021Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Seriescited by 30
  20. 2024CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variablescited by 25
  21. 2025Multi-modal Time Series Analysis: A Tutorial and Surveycited by 24
  22. 2022Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecastingcited by 23
  23. 2021A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecastingcited by 21
  24. 2023DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecastingcited by 21
  25. 2021Adjusting for Autocorrelated Errors in Neural Networks for Time Seriescited by 18
  26. 2024S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecastingcited by 17
  27. 2021RNN with Particle Flow for Probabilistic Spatio-temporal Forecastingcited by 16
  28. 2022TiSAT: Time Series Anomaly Transformercited by 15
  29. 2025Positional Encoding in Transformer-Based Time Series Models: A Surveycited by 14
  30. 2021Non-linear Functional Modeling using Neural Networkscited by 13
  31. 2021Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equationscited by 13
  32. 2024Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adaptcited by 13
  33. 2026Reverso: Efficient Time Series Foundation Models for Zero-shot Forecastingcited by 13
  34. 2021Non-Gaussian Gaussian Processes for Few-Shot Regressioncited by 12
  35. 2022Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Predictioncited by 12
  36. 2022Recognition and Prediction of Surgical Gestures and Trajectories Using Transformer Models in Robot-Assisted Surgerycited by 12
  37. 2025Retrieval-augmented Large Language Models for Financial Time Series Forecastingcited by 12
  38. 2021Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecastingcited by 11

50 most cited of 245 All 245 (JSON)