In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks.
Shangqing Xu|Harshavardhan Kamarthi|Haoxin Liu|B. Aditya Prakash
| Anthology ID: | DBLP:conf/cikm/XuK0P25 |
|---|---|
| Volume: | Proceedings of the 34th ACM International Conference on Information and Knowledge Management, CIKM 2025, Seoul, Republic of Korea, November 10-14, 2025 |
| Year: | 2025 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 5386-5390 |
| URL: | https://doi.org/10.1145/3746252.3760801 |
| DOI: | https://doi.org/10.1145/3746252.3760801 |
| DBLP: | conf/cikm/XuK0P25 |
| BibTeX: |
@inproceedings{xu-2025-incontext,
author = {Shangqing Xu and
Harshavardhan Kamarthi and
Haoxin Liu and
B. Aditya Prakash},
editor = {Meeyoung Cha and
Carl Yang and
Senjuti Basu Roy and
Chanyoung Park and
Zhenhui Jessie Li and
Noseong Park and
Carl Yang and
Senjuti Basu Roy and
Zhenhui Jessie Li and
Jaap Kamps and
Kijung Shin and
Bryan Hooi and
Lifang He},
title = {{In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks}},
booktitle = {{Proceedings of the 34th ACM International Conference on Information and Knowledge Management, CIKM 2025, Seoul, Republic of Korea, November 10-14, 2025}},
pages = {5386--5390},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3746252.3760801},
doi = {https://doi.org/10.1145/3746252.3760801}
}