most citedApplication of an ANN and LSTM-based Ensemble Model for Stock Market Prediction

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 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…