Search Results for author: Gang Kou

Found 6 papers, 3 papers with code

Bankruptcy Prediction via Mixing Intra-Risk and Conductive-Risk

1 code implementation1 Feb 2022 Yu Zhao, Shaopeng Wei, Yu Guo, Qing Yang, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

Afterward, we propose an enterprise conductive-risk encoder based on enterprise relational information from the enterprise knowledge graph for its conductive-risk embedding.

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

1 code implementation11 Jan 2022 Yu Zhao, Huaming Du, Ying Liu, Shaopeng Wei, Xingyan Chen, Fuzhen Zhuang, Qing Li, Ji Liu, Gang Kou

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets.

Stock Prediction

Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks

1 code implementation24 Dec 2021 Yu Zhao, Shaopeng Wei, Huaming Du, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

To address this issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-class and inter-class attention-based encoder under a hierarchical mechanism.

Graph Learning

Demand Forecasting in Bike-sharing Systems Based on A Multiple Spatiotemporal Fusion Network

no code implementations23 Sep 2020 Xiao Yan, Gang Kou, Feng Xiao, Dapeng Zhang, Xianghua Gan

Spatial and temporal features are critical for demand forecasting in BSSs, but it is challenging to extract spatiotemporal dynamics.

Ensemble Learning Feature Importance

Learning Spatiotemporal Features of Ride-sourcing Services with Fusion Convolutional Network

no code implementations15 Apr 2019 Feng Xiao, Dapeng Zhang, Gang Kou, Lu Li

To collectively forecast the demand for ride-sourcing services in all regions of a city, the deep learning approaches have been applied with commendable results.

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