Stock-Price Forecasting Based on XGBoost and LSTM-Reference-Cited by-同舟云学术

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Stock-Price Forecasting Based on XGBoost and LSTM

Published:2022 Issue:1 Volume:40 Page:237-246
ISSN:0267-6192
Container-title:Computer Systems Science and Engineering
language:en
Short-container-title:

Author:

Hoang Vuong Pham,Tan Dat Trinh,Khoi Mai Tieu,Hoang Uyen Pham,The Bao Pham

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

General Computer Science,Theoretical Computer Science,Control and Systems Engineering

Link

https://www.techscience.com/csse/v40n1/44219/pdf

Cited by 30 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of deep learning and statistical methods in predicting Taiwanese stock trends;Journal of Computational Methods in Sciences and Engineering;2024-06-17

2. Stock Market Prediction: XGBoost and LSTM Comparative Analysis;2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT);2024-05-03

3. A novel hybrid optimization ensemble learning approach for energy futures price forecasting;Journal of Intelligent & Fuzzy Systems;2024-03-05

4. Agricultural commodity futures prices prediction based on a new hybrid forecasting model combining quadratic decomposition technology and LSTM model;Frontiers in Sustainable Food Systems;2024-02-19

5. An interval constraint-based trading strategy with social sentiment for the stock market;Financial Innovation;2024-02-10

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