Jul 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 38 references
Computer Science
TL;DR
This paper proposes MFSNet, a lightweight forecasting framework that combines hidden-space multi-scale representation learning with differentiable frequency-domain suppression, and shows its potential as a practical lightweight solution for multivariate time series forecasting.
Benefiting from the linear-complexity Mamba architecture and hierarchical latent representation fusion, UMambaNet provides an effective balance between forecasting performance and computational efficiency.
Xiao-He Wu, Kun Zhang, Hui Chen et al.· Journal of Supercomputing· 0 citations
The Decomposed Recurrent Neural Network (DeRNN) is proposed, which decouples global trend modeling from local fluctuation extraction via an asymmetric dual-track architecture and exhibits superior robustness against noise and distribution shifts.
Shanyun Qian· Poster Volume 0008 The 2026...· 0 citations
Multivariate time series data constitutes the fundamental basis for decision making across real-world scenarios such as financial trading, intensive care, energy dispatch and the Internet of Things. Traditional statistical methods and early deep learning architectures including recurrent neural networks and convolution...
De-Liang Zhang, Bo He, Yu-Xin Gao· Journal of Computing and Ele...· 0 citations
SETTer is introduced, a transformer-based model that addresses challenges of long-term multivariate time series forecasting by incorporating novel techniques for decoupled self-attention and hybrid masking and outperforms state-of-the-art models in 88% of the scenarios.
Abraham Ezema, C. Eze, F. Ponci et al.· 0 citations
Results support the effectiveness of coordinated SDG and temporal-frequency fusion under the evaluated clean and controlled synthetic degradation conditions.
Min-Seon Chae, Tae-Hyoung Park· Italian National Conference...· 0 citations
This paper validates that the integrated multiscale attention convolutional network and LSTM framework yield a mean absolute error of 8.8 kW and a root mean square error of 15.5 kW during peak periods, outperforming single-scale architectures.
Hao-Yang Xie, Xi Chen· Mechanics & Industry· 0 citations
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