HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting.
Shubao Zhao|Ming Jin|Zhaoxiang Hou|Chengyi Yang|Zengxiang Li|Qingsong Wen|Yi Wang
| Anthology ID: | DBLP:conf/cikm/Zhao0HYLW024 |
|---|---|
| Volume: | Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024 |
| Year: | 2024 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 3352-3362 |
| URL: | https://doi.org/10.1145/3627673.3679741 |
| DOI: | https://doi.org/10.1145/3627673.3679741 |
| DBLP: | conf/cikm/Zhao0HYLW024 |
| BibTeX: |
@inproceedings{zhao-2024-himtm,
author = {Shubao Zhao and
Ming Jin and
Zhaoxiang Hou and
Chengyi Yang and
Zengxiang Li and
Qingsong Wen and
Yi Wang},
editor = {Edoardo Serra and
Francesca Spezzano},
title = {{HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting}},
booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024}},
pages = {3352--3362},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3627673.3679741},
doi = {https://doi.org/10.1145/3627673.3679741}
}