A Params-Per-Pair diagnostic is introduced that predicts from dataset properties alone whether structural priors will help and reveals a horizon-dependent complementarity: the structural prior contributes 33% of the gain at short horizons but 88% at long horizons, confirming that time-invariant knowledge compensates as temporal signal fades.
DAT-TimeXer is proposed, a structure-aware adaptation of TimeXer for closing-price forecasting that achieves the lowest mean forecasting errors among the compared models in the one-step evaluations and maintains lower errors at horizons of 1, 3, 5, and 10 in the evaluated multi-step tasks.
Si-Xing Liu, Quan-Xiang Lan, Jing Zhang et al.· Complex & Intelligent Sy...· 0 citations
A Constraint-Guided Dynamic Graph Network (ConDyGNet), whose core idea is “global basis, dynamic weights”, which learns a low-rank global basis as a shared structural constraint and generates patch-wise basis mixing weights to construct dynamic propagation graphs.
Zhen-Zhou Li, Xiang Li, Zhibin Niu· Proceedings of the Thirty-Fi...· 0 citations
The findings indicate that the proposed architecture successfully reconciles multi-scale feature extraction with lightweight dependency modeling, enhancing structural generalization and providing a scalable framework for real-time temporal analysis in complex industrial environments.
This work proposes CvLoss, a plug-in structural regularizer that constrains forecast residuals on a cross-variable graph and shows that CvLoss consistently improves competitive forecasting models, outperforms representative learning objectives, and is compatible with a variety of forecasting backbones.
Kuiye Ding, Yifan Hu, Hanchen Wang et al.· 1 citation
CoRe is proposed, a model-agnostic learning objective for direct multivariate forecasting that replaces pointwise supervision with two output-space constraints: a frequency coherence loss that aligns predicted and target spectra, and a low-rank relational graph loss that matches sampled pairwise differences in a target...
Xiao-Yu Lin, Hui-Ran Duan, Yi-Ning Liu et al.· 0 citations
The findings indicate that passing attention-derived context into a bidirectional memory module offers a practical means of combining long-horizon structure with local temporal variation, although computational cost remains relevant for latency-sensitive trading applications.
Hao Wu· AI and Data Science Journal· 0 citations
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