Work

DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting

Year: 2025

Type: conference-paper

Abstract: 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.

Source: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

DOI: 10.1145/3690624.3709325

Authors: Xiangfei Qiu, Xingjian Wu, Yan Lin, Chenjuan Guo, Jilin Hu, Bin Yang

Institutions: East China Normal University, Aalborg University

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

OpenAlex ID: W4409158620

FWCI: 79.93

Cites: 43

Cited by: 82

Related to: 10