Temporal-Relational hypergraph tri-Attention networks for stock trend prediction

Cui, Chaoran; Li, Xiaojie; Du, Juan; Zhang, Chunyun; Nie, Xiushan; Wang, Meng; Yin, Yilong

doi:10.1016/j.patcog.2023.109759

Cited by 10 publications

(1 citation statement)

References 17 publications

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“…Scholars have combined hypergraph attention networks with gated recurrent unit networks [35] to capture higher-order correlations and temporal features. In addition, to further enhance the performance and scalability of hypergraph attention networks, Cui et al [36] enhanced hypergraph convolution networks by hierarchical organization of intra-hyperedge, inter-hyperedge and inter-hypergraph attention modules to reduce information loss. These innovative research works have expanded a new possibility for hypergraph attention networks in handling challenging tasks with complex relationships.…”

Section: Hypergraph Attention Networkmentioning

confidence: 99%

Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation

Rong,

Nong,

Liang

et al. 2024

Applied Sciences

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Point cloud segmentation, as the basis for 3D scene understanding and analysis, has made significant progress in recent years. Graph-based modeling and learning methods have played an important role in point cloud segmentation. However, due to the inherent complexity of point cloud data, it is difficult to capture higher-order and complex features of 3D data using graph learning methods. In addition, how to quickly and efficiently extract important features from point clouds also poses a great challenge to the current research. To address these challenges, we propose a new framework, called hypergraph position attention convolution networks (HGPAT), for point cloud segmentation. Firstly, we use hypergraph to model the higher-order relationships among point clouds. Secondly, in order to effectively learn the feature information of point cloud data, a hyperedge position attention convolution module is proposed, which utilizes the hyperedge–hyperedge propagation pattern to extract and aggregate more important features. Finally, we design a ResNet-like module to reduce the computational complexity of the network and improve its efficiency. We have conducted point cloud segmentation experiments on the ShapeNet Part and S3IDS datasets, and the experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art ones.

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Section: Hypergraph Attention Networkmentioning

confidence: 99%

Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation

Rong,

Nong,

Liang

et al. 2024

Applied Sciences

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DeepAR-Attention probabilistic prediction for stock price series

Li,

Chen,

Zhou

et al. 2024

Neural Comput & Applic

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Generalized spatial–temporal regression graph convolutional transformer for traffic forecasting

Xiong,

Su,

Zeng

et al. 2024

Complex Intell. Syst.

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Spatial–temporal data is widely available in intelligent transportation systems, and accurately solving non-stationary of spatial–temporal regression is critical. In most traffic flow prediction research, the non-stationary solution of deep spatial–temporal regression tasks is typically formulated as a spatial–temporal graph modeling problem. However, there are several issues: (1) the coupled spatial–temporal regression approach renders it unfeasible to accurately learn the dependencies of diverse modalities; (2) the intricate stacking design of deep spatial–temporal network modules limits the interpretation and migration capability; (3) the ability to model dynamic spatial–temporal relationships is inadequate. To tackle the challenges mentioned above, we propose a novel unified spatial–temporal regression framework named Generalized Spatial–Temporal Regression Graph Convolutional Transformer (GSTRGCT) that extends panel model in spatial econometrics and combines it with deep neural networks to effectively model non-stationary relationships of spatial–temporal regression. Considering the coupling of existing deep spatial–temporal networks, we introduce the tensor decomposition to explicitly decompose the panel model into a tensor product of spatial regression on the spatial hyper-plane and temporal regression on the temporal hyper-plane. On the spatial hyper-plane, we present dynamic adaptive spatial weight network (DASWNN) to capture the global and local spatial correlations. Specifically, DASWNN adopts spatial weight neural network (SWNN) to learn the semantic global spatial correlation and dynamically adjusts the local changing spatial correlation by multiplying between spatial nodes embedding. On the temporal hyper-plane, we introduce the Auto-Correlation attention mechanism to capture the period-based temporal dependence. Extensive experiments on the two real-world traffic datasets show that GSTRGCT consistently outperforms other competitive methods with an average of 62% and 59% on predictive performance.

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Temporal-Relational hypergraph tri-Attention networks for stock trend prediction

Cited by 10 publications

References 17 publications

Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation

Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation

DeepAR-Attention probabilistic prediction for stock price series

Generalized spatial–temporal regression graph convolutional transformer for traffic forecasting

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