全局-局部特征融合的多变量时序预测方法

doi:10.16451/j.cnki.issn1003-6059.202605002

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Abstract The Transformer demonstrates considerable potential in time series analysis. However, the attention mechanism of the Transformer often aggregates semantically irrelevant query-key pairs, thereby resulting in the degradation of prediction performance. Moreover, complex patterns in time series, including periodicity and abrupt fluctuations, pose additional challenges for effective modeling. To address these issues, a multivariate time series forecasting method with global-local feature fusion(MTS-GLFF) is proposed. First, a TopK selection operator is designed. It dynamically generates sparse masks based on learnable sensor embeddings, thereby retaining key sequences for subsequent feature aggregation. Next, a dual-branch time series forecasting framework comprising global and local branch networks is constructed. The global branch captures global interaction features through a cross-variable attention mechanism, while the local branch adopts a multi-scale architecture that decomposes time series into multi-granularity patterns for fine-grained modeling of local dependencies. Experiments on 10 benchmark datasets demonstrate that MTS-GLFF achieves competitive performance.

Key words： Time Series Sparse Mask Attention Mechanism Feature Fusion Transformer

Received: 30 January 2026

ZTFLH:

TP183

Fund:National Natural Science Foundation of China(No.62262006), Guangxi Key Research and Development Program(No.AB24010112,AB23026048)

Corresponding Authors: QIANG Baohua, Ph.D., professor.His research interests include big data analytics, time series forecasting, and data mining.

About author:: XIE Yuan, Ph.D. candidate.His research interests include time series representation learning, time series forecasting and anomaly detection, and data mining.
ZHANG Shihao, Ph.D., lecturer. His research interests include computer vision and time series analysis.
CHEN Ruidong, Ph.D. candidate. His research interests include deep learning and cross-modal analysis.
CHEN Lirui, Ph.D. candidate. His research interests include deep learning and computer vision.

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	XIE Yuan
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	ZHANG Wenhui

Cite this article:

XIE Yuan,QIANG Baohua,ZHANG Shihao等. Multivariate Time Series Forecasting Method with Global-Local Feature Fusion[J]. Pattern Recognition and Artificial Intelligence, 2026, 39(5): 397-414.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202605002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2026/V39/I5/397

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