mlsanityData to 14 Oct 2026

TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

arXiv 2403.09898 ↗First posted 14 Mar 2024cs.LGcited by 64

On the map: State-space sequence models (S4, Mamba) → Time series forecasting with SSMs

Abstract (arXiv)

Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency. We introduce TimeMachine, an innovative model that leverages Mamba, a state-space model, to capture long-term dependencies in multivariate time series data while maintaining linear scalability and small memory footprints. TimeMachine exploits the unique properties of time series data to produce salient contextual cues at multi-scales and leverage an innovative integrated quadruple-Mamba architecture to unify the handling of channel-mixing and channel-independence situations, thus enabling effective selection of contents for prediction against global and local contexts at different scales. Experimentally, TimeMachine achieves superior performance in prediction accuracy, scalability, and memory efficiency, as extensively validated using benchmark datasets. Code availability: https://github.com/Atik-Ahamed/TimeMachine

Key facts. Cited by 64 papers in the map (data through 14 Oct 2026). Placed in Time series forecasting with SSMs, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 29 of its 36 references that are in the map. First posted on arXiv 14 Mar 2024.

Builds on

29 of 36 references.

References · 29

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  5. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  6. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  7. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  8. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  9. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  10. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  11. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  12. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
17 more
  1. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 950
  2. 2021Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layerscited by 608
  3. 2021TS2Vec: Towards Universal Representation of Time Seriescited by 471
  4. 2020A Transformer-based Framework for Multivariate Time Series Representation Learningcited by 453
  5. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 419
  6. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 391
  7. 2021Time-Series Representation Learning via Temporal and Contextual Contrastingcited by 345
  8. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 338
  9. 2023Long-term Forecasting with TiDE: Time-series Dense Encodercited by 332
  10. 2019Unsupervised Scalable Representation Learning for Multivariate Time Seriescited by 269
  11. 2021Unsupervised Representation Learning for Time Series with Temporal Neighborhood Codingcited by 224
  12. 2024Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modelingcited by 113
  13. 2022Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusioncited by 96
  14. 2024The Hidden Attention of Mamba Modelscited by 91
  15. 2024Graph Mamba: Towards Learning on Graphs with State Space Modelscited by 86
  16. 2022Flowformer: Linearizing Transformers with Conservation Flowscited by 69
  17. 2024Universal Time-Series Representation Learning: A Surveycited by 30

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 30; 2025: 22; 2026: 12.

64 papers.

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By area:

  1. 2024Is Mamba Effective for Time Series Forecasting?cited by 114
  2. 2024Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challengescited by 64
  3. 2024State Space Model for New-Generation Network Alternative to Transformers: A Surveycited by 59
  4. 2024A Survey of Mambacited by 39
  5. 2024Mambular: A Sequential Model for Tabular Deep Learningcited by 21
  6. 2024MADiff: Motion-Aware Mamba Diffusion Models for Hand Trajectory Prediction on Egocentric Videoscited by 18
  7. 2024SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecastingcited by 16
  8. 2025TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-statecited by 16
  9. 2024Bi-Mamba+: Bidirectional Mamba for Time Series Forecastingcited by 15
  10. 2024MambaTS: Improved Selective State Space Models for Long-term Time Series Forecastingcited by 14
  11. 2024HARMamba: Efficient and Lightweight Wearable Sensor Human Activity Recognition Based on Bidirectional Mambacited by 12
  12. 2024TSCMamba: Mamba Meets Multi-View Learning for Time Series Classificationcited by 10
38 more
  1. 2026The Forecast After the Forecast: A Post-Processing Shift in Time Seriescited by 10
  2. 2024Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecastingcited by 9
  3. 2025Multimodal Conditioned Diffusive Time Series Forecastingcited by 9
  4. 2026Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Modelscited by 9
  5. 2024OPa-Ma: Text Guided Mamba for 360-degree Image Out-paintingcited by 7
  6. 2024Integration of Mamba and Transformer -- MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamicscited by 7
  7. 2024On the Efficiency of NLP-Inspired Methods for Tabular Deep Learningcited by 7
  8. 2024Mamba4KT:An Efficient and Effective Mamba-based Knowledge Tracing Modelcited by 6
  9. 2024Test Time Learning for Time Series Forecastingcited by 6
  10. 2025ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecastingcited by 6
  11. 2024Sequential Order-Robust Mamba for Time Series Forecastingcited by 5
  12. 2024Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devicescited by 5
  13. 2024PowerMamba: A Deep State Space Model and Comprehensive Benchmark for Time Series Prediction in Electric Power Systemscited by 4
  14. 2025Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecastingcited by 4
  15. 2026MambaSL: Exploring Single-Layer Mamba for Time Series Classificationcited by 4
  16. 2024UmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mambacited by 3
  17. 2024LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecastingcited by 3
  18. 2024Dataset-Driven Channel Masks in Transformers for Multivariate Time Seriescited by 3
  19. 2025Channel Normalization for Time Series Channel Identificationcited by 3
  20. 2026Mamba-CAD: State Space Model For 3D Computer-Aided Design Generative Modelingcited by 3
  21. 2026Reasoning-Aware Training for Time Series Forecastingcited by 3
  22. 2025Technologies on Effectiveness and Efficiency: A Survey of State Spaces Modelscited by 2
  23. 2024Venturing into Uncharted Waters: The Navigation Compass from Transformer to Mambacited by 1
  24. 2024Integrating the Expected Future in Load Forecasts with Contextually Enhanced Transformer Modelscited by 1
  25. 2024TiVaT: A Transformer with a Single Unified Mechanism for Capturing Asynchronous Dependencies in Multivariate Time Series Forecastingcited by 1
  26. 2024MTS-UNMixers: Multivariate Time Series Forecasting via Channel-Time Dual Unmixingcited by 1
  27. 2025Scaling Transformers for Time Series Forecasting: Do Pretrained Large Models Outperform Small-Scale Alternatives?cited by 1
  28. 2025Data Augmentation in Time Series Forecasting through Inverted Frameworkcited by 1
  29. 2025PIE: Perception and Interaction Enhanced End-to-End Motion Planning for Autonomous Drivingcited by 1
  30. 2025SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Managementcited by 1
  31. 2026DMamba: Decomposition-enhanced Mamba for Time Series Forecastingcited by 1
  32. 2024ISMRNN: An Implicitly Segmented RNN Method with Mamba for Long-Term Time Series Forecastingcited by 0
  33. 2024Revisiting Attention for Multivariate Time Series Forecastingcited by 0
  34. 2024A SSM is Polymerized from Multivariate Time Seriescited by 0
  35. 2025Progressive Supervision via Label Decomposition: An Long-Term and Large-Scale Wireless Traffic Forecasting Methodcited by 0
  36. 2025GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecturecited by 0
  37. 2025Attention Mamba: Time Series Modeling with Adaptive Pooling Acceleration and Receptive Field Enhancementscited by 0
  38. 2025Bidirectional Linear Recurrent Models for Sequence-Level Multisource Fusioncited by 0

50 most cited of 64 All 64 (JSON)

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  1. 2024A Mamba Foundation Model for Time Series Forecasting
  2. 2024CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting
  3. 2026UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
  4. 2025ParallelTime: Dynamically Weighting the Balance of Short- and Long-Term Temporal Dependencies
  5. 2025ms-Mamba: Multi-scale Mamba for Time-Series Forecasting
  6. 2025Repetitive Contrastive Learning Enhances Mamba's Selectivity in Time Series Prediction
  7. 2025Mamba time series forecasting with uncertainty quantification
  8. 2026Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling