16 citations · 21 across the 5 of their papers we have counts for
5 papers
Gradient Boosting Decision Tree with LSTM for Investment Prediction
Chang Yu, Fang Liu, Jie Zhu +5
This paper proposes a hybrid framework combining LSTM (Long Short-Term Memory) networks with LightGBM and CatBoost for stock price prediction. The framework processes time-series f…
Application of an ANN and LSTM-based Ensemble Model for Stock Market Prediction
Fang Liu, Shaobo Guo, Qianwen Xing +5
Stock trading has always been a key economic indicator in modern society and a primary source of profit for financial giants such as investment banks, quantitative trading firms, a…
Enhanced Credit Score Prediction Using Ensemble Deep Learning Model
Qianwen Xing, Chang Yu, Sining Huang +3
In contemporary economic society, credit scores are crucial for every participant. A robust credit evaluation system is essential for the profitability of core businesses such as c…
Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN
Chang Yu, Yixin Jin, Qianwen Xing +3
Bank credit risk is a significant challenge in modern financial transactions, and the ability to identify qualified credit card holders among a large number of applicants is crucia…
Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integrated
Qi Zheng, Chang Yu, Jin Cao +3
With the rise of various online and mobile payment systems, transaction fraud has become a significant threat to financial security. This study explores the application of advanced…