UniVQ: Vector quantization is effective for unified time series forecasting
- DOI
- 10.1016/j.neunet.2026.109704
- Published
- 2027-02
- Container
- Neural Networks
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109704,
title = {UniVQ: Vector quantization is effective for unified time series forecasting},
author = {Xiaoyuan Zhang and Ziwen Chen and Wei Liu and Ming Zhu},
year = {2027},
journal = {Neural Networks},
doi = {10.1016/j.neunet.2026.109704},
url = {https://doi.org/10.1016/j.neunet.2026.109704}
}RIS
TY - JOUR TI - UniVQ: Vector quantization is effective for unified time series forecasting AU - Xiaoyuan Zhang AU - Ziwen Chen AU - Wei Liu AU - Ming Zhu PY - 2027 JO - Neural Networks DO - 10.1016/j.neunet.2026.109704 UR - https://doi.org/10.1016/j.neunet.2026.109704 ER -
APA
Zhang, X., Chen, Z., Liu, W., & Zhu, M. (2027). UniVQ: Vector quantization is effective for unified time series forecasting. Neural Networks. https://doi.org/10.1016/j.neunet.2026.109704
Source records
- crossref · retrieved 2026-11-05T19:10:44.237Z