DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
Multivariate time series forecasting is crucial for various applications, such as financial investment, energy management, weather forecasting, and traffic optimization. However, accurate forecasting is challenging due to two main factors. First, real-world time series often show heterogeneous temporal patterns caused by distribution shifts over time. Second, correlations among channels are complex and intertwined, making it hard to model the interactions among channels precisely and flexibly.
Authors
- Xingjian Wu (ORCID: https://orcid.org/0009-0007-1879-4653)
- Jilin Hu (ORCID: https://orcid.org/0000-0002-7739-7769)
- Xiangfei Qiu
- Bin Yang (ORCID: https://orcid.org/0000-0002-1658-1079)
- Chenjuan Guo (ORCID: https://orcid.org/0000-0002-4516-4637)
- Yan Lin (ORCID: https://orcid.org/0000-0002-2320-9777)
Institutions
- East China Normal University (CN)
- Aalborg University (DK)
Publication Details
- Published
- 2025-04-04
- DOI
- https://doi.org/10.1145/3690624.3709325
- Citations
- 27
- Primary Topic
- Time Series Analysis and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 40.10
Funders
- National Natural Science Foundation of China