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HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting (2401.05012v2)

Published 10 Jan 2024 in cs.LG

Abstract: Time series forecasting is a critical and challenging task in practical application. Recent advancements in pre-trained foundation models for time series forecasting have gained significant interest. However, current methods often overlook the multi-scale nature of time series, which is essential for accurate forecasting. To address this, we propose HiMTM, a hierarchical multi-scale masked time series modeling with self-distillation for long-term forecasting. HiMTM integrates four key components: (1) hierarchical multi-scale transformer (HMT) to capture temporal information at different scales; (2) decoupled encoder-decoder (DED) that directs the encoder towards feature extraction while the decoder focuses on pretext tasks; (3) hierarchical self-distillation (HSD) for multi-stage feature-level supervision signals during pre-training; and (4) cross-scale attention fine-tuning (CSA-FT) to capture dependencies between different scales for downstream tasks. These components collectively enhance multi-scale feature extraction in masked time series modeling, improving forecasting accuracy. Extensive experiments on seven mainstream datasets show that HiMTM surpasses state-of-the-art self-supervised and end-to-end learning methods by a considerable margin of 3.16-68.54\%. Additionally, HiMTM outperforms the latest robust self-supervised learning method, PatchTST, in cross-domain forecasting by a significant margin of 2.3\%. The effectiveness of HiMTM is further demonstrated through its application in natural gas demand forecasting.

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References (43)
  1. Data2vec: A general framework for self-supervised learning in speech, vision and language. In International Conference on Machine Learning, pages 1298–1312. PMLR, 2022.
  2. Deep learning for time series forecasting: Tutorial and literature survey. ACM Computing Surveys, 55(6):1–36, 2022.
  3. Multi-scale adaptive graph neural network for multivariate time series forecasting. IEEE Transactions on Knowledge and Data Engineering, 2023.
  4. Context autoencoder for self-supervised representation learning. International Journal of Computer Vision, pages 1–16, 2023.
  5. Long sequence time-series forecasting with deep learning: A survey. Information Fusion, 97:101819, 2023.
  6. Timemae: Self-supervised representations of time series with decoupled masked autoencoders. arXiv preprint arXiv:2303.00320, 2023.
  7. Multi-scale convolutional neural networks for time series classification. arXiv preprint arXiv:1603.06995, 2016.
  8. Simmtm: A simple pre-training framework for masked time-series modeling. arXiv preprint arXiv:2302.00861, 2023.
  9. Adarnn: Adaptive learning and forecasting of time series. In Proceedings of the 30th ACM international conference on information & knowledge management, pages 402–411, 2021.
  10. Preformer: predictive transformer with multi-scale segment-wise correlations for long-term time series forecasting. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE, 2023.
  11. Label-efficient time series representation learning: A review. arXiv preprint arXiv:2302.06433, 2023.
  12. Self-supervised representation learning: Introduction, advances, and challenges. IEEE Signal Processing Magazine, 39(3):42–62, 2022.
  13. Time-series data mining. ACM Computing Surveys (CSUR), 45(1):1–34, 2012.
  14. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271–21284, 2020.
  15. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729–9738, 2020.
  16. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000–16009, 2022.
  17. Large models for time series and spatio-temporal data: A survey and outlook. arXiv preprint arXiv:2310.10196, 2023.
  18. Smartformer: Semi-autoregressive transformer with efficient integrated window attention for long time series forecasting. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pages 2169–2177, 2023.
  19. Ti-mae: Self-supervised masked time series autoencoders. arXiv preprint arXiv:2301.08871, 2023.
  20. Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A, 379(2194):20200209, 2021.
  21. Frequency-aware masked autoencoders for multimodal pretraining on biosignals. arXiv preprint arXiv:2309.05927, 2023.
  22. A survey on time-series pre-trained models. arXiv preprint arXiv:2305.10716, 2023.
  23. A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint arXiv:2211.14730, 2022.
  24. Masked autoencoders for point cloud self-supervised learning. In European conference on computer vision, pages 604–621. Springer, 2022.
  25. Scaleformer: iterative multi-scale refining transformers for time series forecasting. arXiv preprint arXiv:2206.04038, 2022.
  26. Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1567–1577, 2022.
  27. Micn: Multi-scale local and global context modeling for long-term series forecasting. In The Eleventh International Conference on Learning Representations, 2022.
  28. Learning latent seasonal-trend representations for time series forecasting. Advances in Neural Information Processing Systems, 35:38775–38787, 2022.
  29. Transformers in time series: A survey. arXiv preprint arXiv:2202.07125, 2022.
  30. Transformers in time series: A survey. In International Joint Conference on Artificial Intelligence (IJCAI), 2023.
  31. Cost: Contrastive learning of disentangled seasonal-trend representations for time series forecasting. arXiv preprint arXiv:2202.01575, 2022.
  32. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems, 34:22419–22430, 2021.
  33. Timesnet: Temporal 2d-variation modeling for general time series analysis. In The Eleventh International Conference on Learning Representations, 2022.
  34. Ts2vec: Towards universal representation of time series. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 8980–8987, 2022.
  35. Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023.
  36. 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.
  37. Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In The Eleventh International Conference on Learning Representations, 2022.
  38. Self-supervised contrastive pre-training for time series via time-frequency consistency. Advances in Neural Information Processing Systems, 35:3988–4003, 2022.
  39. A survey on masked autoencoder for visual self-supervised learning. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pages 6805–6813, 2023.
  40. Self-supervised learning for time series analysis: Taxonomy, progress, and prospects. arXiv preprint arXiv:2306.10125, 2023.
  41. Multi-resolution time-series transformer for long-term forecasting. arXiv preprint arXiv:2311.04147, 2023.
  42. Simts: Rethinking contrastive representation learning for time series forecasting. arXiv preprint arXiv:2303.18205, 2023.
  43. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning, pages 27268–27286. PMLR, 2022.
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Authors (7)
  1. Shubao Zhao (4 papers)
  2. Ming Jin (130 papers)
  3. Zhaoxiang Hou (6 papers)
  4. Chengyi Yang (11 papers)
  5. Zengxiang Li (17 papers)
  6. Qingsong Wen (139 papers)
  7. Yi Wang (1038 papers)

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