GAS-Norm: Score-Driven Adaptive Normalization for Non-Stationary Time Series Forecasting in Deep Learning.
Edoardo Urettini|Daniele Atzeni|Reshawn Ramjattan|Antonio Carta
| Anthology ID: | DBLP:conf/cikm/UrettiniARC24 |
|---|---|
| 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: | 2282-2291 |
| URL: | https://doi.org/10.1145/3627673.3679822 |
| DOI: | https://doi.org/10.1145/3627673.3679822 |
| DBLP: | conf/cikm/UrettiniARC24 |
| BibTeX: |
@inproceedings{urettini-2024-gasnorm,
author = {Edoardo Urettini and
Daniele Atzeni and
Reshawn Ramjattan and
Antonio Carta},
editor = {Edoardo Serra and
Francesca Spezzano},
title = {{GAS-Norm: Score-Driven Adaptive Normalization for Non-Stationary Time Series Forecasting in Deep Learning}},
booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024}},
pages = {2282--2291},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3627673.3679822},
doi = {https://doi.org/10.1145/3627673.3679822}
}