2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2025
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…
cs.CE2024★ 1 cited
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…
cs.LG2024★ 2 cited
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…