Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting-Reference-Cited by-同舟云学术

Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting

Published:2024-08 Issue:8 Volume:36 Page:3783-3800
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Deng Jinliang¹^ORCID,Chen Xiusi²^ORCID,Jiang Renhe³^ORCID,Du Yin ⁴^ORCID,Yang Yi⁵^ORCID,Song Xuan⁶^ORCID,Tsang Ivor W.⁷^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China

2. University of California, Los Angeles, CA, USA

3. Center for Spatial Information Science, University of Tokyo, Bunkyo, Tokyo, Japan

4. Department of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia

5. Tencent WXG, Guangzhou, Guangdong, China

6. SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, Guangdong, China

7. Center for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore

Funder

National Key Research and Development Project of China

Guangdong Provincial Key Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/69/10589999/10457027.pdf?arnumber=10457027

Reference71 articles.

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