个人信息
参与实验室科研项目
人机智能协同关键技术及其在智能制造中的应用
非可信智能驱动的可靠智造
研究课题
基于多尺度特征提取的滚动轴承剩余使用寿命预测方法研究
学术成果
共撰写/参与撰写专利 0 项，录用/发表论文 1 篇，投出待录用论文0篇。
Conference Articles
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DC-Mamber: A Dual Channel Prediction Model Based on Mamba and Linear Transformer for Multivariate Time Series Forecasting
Bing Fan,
Shusen Ma,
and Yun-Bo Zhao
In IEEE ICASSP 2026
2026
[Abs]
[pdf]
In multivariate time series forecasting (MTSF), existing strategies for processing sequences are typically categorized as channel-independent and channel-mixing. The former treats all temporal information of each variable as a token, focusing on capturing local temporal features of individual variables, while the latter constructs a token from the multivariate information at each time step, emphasizing the modeling of global temporal dependencies. Current mainstream models are mostly based on Transformer and the emerging Mamba. Transformers excel at modeling global dependencies through self-attention mechanisms but exhibit limited sensitivity to local temporal patterns and suffer from quadratic computational complexity, restricting their efficiency in longsequence processing. In contrast, Mamba, based on selective state space models (SSMs), achieves linear complexity and efficient long-range modeling but struggles to aggregate global contextual information in parallel. To overcome the limitations of both models, we propose DC-Mamber, a dual-channel forecasting model based on Mamba and linear Transformer for time series forecasting. Specifically, the Mamba-based channel employs a channel-independent strategy to extract intra-variable features, while the Transformer-based channel adopts a channel-mixing strategy to model cross-timestep global dependencies. DC-Mamber first maps the raw input into two distinct feature representations via separate embedding layers. These representations are then processed by a variable encoder (built on Mamba) and a temporal encoder (built on linear Transformer), respectively. Finally, a fusion layer integrates the dual-channel features for prediction. Extensive experiments on eight public datasets confirm DCMamber’s superior accuracy over existing models.
博客文章
学位论文
毕业去向
中国科学技术大学， 博士研究生