Sep 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 44 references
Stock Market Forecasting Methods
TL;DR
This work rethink financial risk contagion from the perspective of frequency domain analysis in signal processing and proposes an innovative Frequency-Guided Adaptive Graph Network (FAGNet) framework, proving the effectiveness of frequency domain decoupling.
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
Reliable deployment of graph neural networks requires calibration, out-of-distribution (OOD) detection, and robustness to distribution shift, yet existing methods address these needs with separate models and objectives. We model uncertain node embeddings as random graph signals: graph Fourier filters capture structural...
Fred Xu, Thomas Markovich, Florence Regol et al.· 0 citations
This paper proposes ICSS-AGCN-GRU, a novel deep learning architecture addressing conservative bias and over-smoothing in multivariate time series forecasting. The framework integrates three technical innovations: (1) a modified ICSSGARCH- t algorithm for automatic variance breakpoint detection and regime segmentation;...
Sai Ren, Jiani Heng· International Conference on...· 0 citations
Multiresolution analysis is widely applied to equity markets on the assumption that different frequency bands capture distinct trading behaviors and information flows. Whether those bands reveal structurally distinct equity communities, or mostly re-express a shared dependence backbone, remains unresolved. We address t...
Much of the recent improvement in multivariate time series forecasting has come from ever larger Transformer models, yet their parameter and compute budgets make them difficult to deploy where resources are limited. This paper presents FreqNet, a compact and interpretable forecaster that operates in the frequency domai...
This paper proposes a structurally regularized causal network framework, denoted by STIC×PCMCI, for directional transmission identification and network-based signal construction in high-dimensional financial time series. The framework uses PCMCI to identify lagged causal relations under multivariate conditioning, while...
Zhen-Hua Liu, Li Lin· Mathematics· 0 citations
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