Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting (2403.09898v2)

Published 14 Mar 2024 in cs.LG
TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

Abstract: 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

PDF HTML Abstract

Exploring the Frontiers of Long-term Time-series Forecasting with TimeMachine

Introduction

The domain of long-term time-series forecasting (LTSF) is etched with myriad challenges, primary among them being the effective capture of long-term dependencies in multivariate time series (MTS), ensuring linear scalability and computational efficiency in model design. The recently introduced TimeMachine model marks a significant stride in addressing these challenges. TimeMachine employs a uniquely designed architecture integrating Mamba, a state-space model (SSM), to efficiently handle long-term dependencies in MTS data. This model not only excels in prediction accuracy but also offers linear scalability and commendability in memory efficiency.

Theoretical Background and Methodology

TimeMachine's foundation lies in the adept exploitation of SSMs' potential to infer sequences over extended periods. Its methodological novelty is encapsulated in the development of an integrated architecture, featuring quadruple Mamba modules. This design is purposefully crafted to address both channel-mixing and channel-independence scenarios in MTS data. By drawing on the unique sequential patterns of time series data, TimeMachine constructs salient contextual cues across multiple scales.

The architecture's crux revolves around two levels of representation, each processed by a pair of Mamba modules. These modules are fine-tuned to sift through global and local contexts, thereby enriching the model with robust predictive capabilities. The model's efficiency is significantly bolstered through a strategic design that minimizes memory footprints while enhancing scalability—attributes primarily attributed to the selective prowess of Mamba modules.

Empirical Validation and Results

The efficacy of the TimeMachine model was rigorously evaluated across benchmark datasets in the LTSF domain, including Weather, Traffic, and Electricity, among others. Its performance was pitted against state-of-the-art models such as iTransformer and PatchTST, revealing TimeMachine's superior capabilities in forecasting accuracy.

Notable findings from these experiments highlight TimeMachine's commendable scalability and memory efficiency, aspects where it noticeably outperforms its counterparts. Particularly in scenarios demanding the processing of MTS data with a considerable number of channels, TimeMachine demonstrates exceptional adeptness, further consolidating its position as a method of choice for LTSF tasks.

Discussion and Implications

The TimeMachine model introduces an innovative approach to LTSF, leveraging the advantages of SSMs in a methodically designed architecture conducive to both channel-mixing and channel-independence scenarios. Its ability to capture long-term dependencies with refined precision, coupled with the scalability and memory efficiency it offers, paves the way for broader applications in various fields reliant on accurate and efficient LTSF.

The practical implications of TimeMachine extend across diverse domains, including but not limited to, weather forecasting, anomaly detection in networks, and strategic planning in energy and agriculture sectors. Moreover, the theoretical contributions of this work shed light on the untapped potential of SSMs in time series forecasting, encouraging further exploration into their capabilities.

Future Directions

While the current iteration of TimeMachine heralds significant advancements in LTSF, the quest for optimization and broadened applicability remains. Future research avenues could explore the integration of TimeMachine in a self-supervised learning framework to further enhance its forecasting prowess. Additionally, tailoring the model to cater to real-time forecasting needs in edge computing scenarios could significantly widen its application spectrum.

In conclusion, TimeMachine stands as a testament to the innovative application of SSMs in tackling the intrinsic complexities of LTSF. Its successful amalgamation of accuracy, scalability, and efficiency heralds a promising direction for future research and applications in the domain of time series forecasting.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (37)
  1. M. A. Ahamed and Q. Cheng. Mambatab: A simple yet effective approach for handling tabular data. arXiv preprint arXiv:2401.08867, 2024.
  2. The hidden attention of mamba models. arXiv preprint arXiv:2403.01590, 2024.
  3. A. Behrouz and F. Hashemi. Graph mamba: Towards learning on graphs with state space models. arXiv preprint arXiv:2402.08678, 2024.
  4. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021.
  5. Time series analysis: forecasting and control. John Wiley & Sons, 2015.
  6. Long-term forecasting with tiDE: Time-series dense encoder. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. URL https://openreview.net/forum?id=pCbC3aQB5W.
  7. Time-series representation learning via temporal and contextual contrasting. arXiv preprint arXiv:2106.14112, 2021.
  8. Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks, 107:3–11, 2018. ISSN 0893-6080. https://doi.org/10.1016/j.neunet.2017.12.012. URL https://www.sciencedirect.com/science/article/pii/S0893608017302976. Special issue on deep reinforcement learning.
  9. Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems, 32, 2019.
  10. Hungry hungry hippos: Towards language modeling with state space models. In International Conference on Learning Representations, 2022.
  11. A. Gu and T. Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023.
  12. Efficiently modeling long sequences with structured state spaces. In International Conference on Learning Representations, 2021a.
  13. Combining recurrent, convolutional, and continuous-time models with linear state space layers. Advances in Neural Information Processing Systems, 34:572–585, 2021b.
  14. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  15. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
  16. Reversible instance normalization for accurate time-series forecasting against distribution shift. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=cGDAkQo1C0p.
  17. D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  18. Revisiting long-term time series forecasting: An investigation on linear mapping. arXiv preprint arXiv:2305.10721, 2023.
  19. Scinet: Time series modeling and forecasting with sample convolution and interaction. Advances in Neural Information Processing Systems, 35:5816–5828, 2022a.
  20. Non-stationary transformers: Exploring the stationarity in time series forecasting. Advances in Neural Information Processing Systems, 35:9881–9893, 2022b.
  21. itransformer: Inverted transformers are effective for time series forecasting. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=JePfAI8fah.
  22. U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722, 2024.
  23. A time series is worth 64 words: Long-term forecasting with transformers. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=Jbdc0vTOcol.
  24. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.
  25. Caduceus: Bi-directional equivariant long-range dna sequence modeling. arXiv preprint arXiv:2403.03234, 2024.
  26. Unsupervised representation learning for time series with temporal neighborhood coding. arXiv preprint arXiv:2106.00750, 2021.
  27. Universal time-series representation learning: A survey. arXiv preprint arXiv:2401.03717, 2024.
  28. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  29. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in neural information processing systems, 34:22419–22430, 2021.
  30. Timesnet: Temporal 2d-variation modeling for general time series analysis. In The eleventh international conference on learning representations, 2022.
  31. L. Yang and S. Hong. Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion. In International conference on machine learning, pages 25038–25054. PMLR, 2022.
  32. Ts2vec: Towards universal representation of time series. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 8980–8987, 2022.
  33. Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023.
  34. A transformer-based framework for multivariate time series representation learning. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pages 2114–2124, 2021.
  35. Y. Zhang and J. Yan. Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In The eleventh international conference on learning representations, 2022.
  36. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 11106–11115, 2021.
  37. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International conference on machine learning, pages 27268–27286. PMLR, 2022.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (2)
  1. Md Atik Ahamed (10 papers)
  2. Qiang Cheng (85 papers)
Citations (36)
View on Semantic Scholar