Stock Price Prediction Based on LSTM- LightGBM Fusion Model

DOI

10.2991/978-94-6463-752-6_29How to use a DOI?

Keywords

Machine Learning; LSTM; LightGBM; Stacking; Stock Price Forecasting

Abstract

This research combines Long Short Term Memory (LSTM) and Light Gradient Boosting Machine (LightGBM) through stacking to develop an LSTM-LightGBM fusion model. Compared with previous research, this study enriches the input data by using new indicators such as the moving average (MA), stochastic indicator (KDJ), and so on. In conclusion, the research findings indicate that the LSTM-LightGBM fusion model shows remarkable stability and superior predictive accuracy. Thus, this fusion model improves stock price forecasting and offers a technical model for investment decision-making in financial markets.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yi’na Huang
PY  - 2025
DA  - 2025/06/07
TI  - Stock Price Prediction Based on LSTM- LightGBM Fusion Model
BT  - Proceedings of 2025 2nd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2025)
PB  - Atlantis Press
SP  - 276
EP  - 283
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-752-6_29
DO  - 10.2991/978-94-6463-752-6_29
ID  - Huang2025
ER  -