Risk Prediction
Risk prediction research aims to develop accurate and reliable models for forecasting future events, spanning diverse domains like finance, healthcare, and transportation. Current efforts focus on leveraging advanced machine learning techniques, including deep learning architectures such as graph neural networks, recurrent neural networks (like LSTMs), and ensemble methods (e.g., combining XGBoost, LightGBM, and TabNet), often enhanced by techniques to address data imbalance and missing values. These advancements hold significant potential for improving decision-making in various sectors by enabling more precise risk assessments and proactive interventions, ultimately leading to better resource allocation and improved outcomes. Furthermore, research emphasizes explainability and fairness in risk prediction models, addressing concerns about bias and transparency.
Papers
Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems
You Wu, Mengfang Sun, Hongye Zheng, Jinxin Hu, Yingbin Liang, Zhenghao Lin
A Novel Methodology in Credit Spread Prediction Based on Ensemble Learning and Feature Selection
Yu Shao, Jiawen Bai, Yingze Hou, Xia'an Zhou, Zhanhao Pan
Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks
D. Cerrone, D. Riccobelli, P. Vitullo, F. Ballarin, J. Falco, F. Acerbi, A. Manzoni, P. Zunino, P. Ciarletta
Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models
Wenying Sun, Zhen Xu, Wenqing Zhang, Kunyuan Ma, You Wu, Mengfang Sun
KACDP: A Highly Interpretable Credit Default Prediction Model
Kun Liu, Jin Zhao
TRIP: Terrain Traversability Mapping With Risk-Aware Prediction for Enhanced Online Quadrupedal Robot Navigation
Minho Oh, Byeongho Yu, I Made Aswin Nahrendra, Seoyeon Jang, Hyeonwoo Lee, Dongkyu Lee, Seungjae Lee, Yeeun Kim, Marsim Kevin Christiansen, Hyungtae Lim, Hyun Myung