Integrating LSTM and Clustering SVM for Enhanced Stock Price Prediction

DOI

10.2991/978-94-6463-823-3_64How to use a DOI?

Keywords

Stock Price Prediction; LSTM; Clustering

Abstract

The study explores the integration model of the Long Short-Term Memory Network (LSTM) and clustering SVM to improve the prediction accuracy of stock prices. This article utilizes the NVIDIA historical dataset from Kaggle and uses Close, High, Low, Open, and Volume as features to predict future changes in the adjusted close price. The study utilizes LSTM networks and demonstrates their effectiveness in solving the problem of time series modeling for financial forecasting. After finding the residuals for different market conditions, K-Means is used as a clustering algorithm for classification, and SVM is further used to complete further refined forecasts. The experiments show that after incorporating the clustering SVM, the hybrid model significantly outperforms the standalone LSTM model in both training and test mean squared error (MSE), which are respectively reduced to 0.59 and 23.45. Furthermore, the Precision, Recall, and F1-score metrics demonstrate that the new hybrid LSTM model has improved accuracy in forecasting the course of the movement of the stock price. The new model could improve stock price forecasts more effectively, providing valuable insights for investors navigating complex 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.

Download article (PDF)

TY  - CONF
AU  - Yumeng Li
PY  - 2025
DA  - 2025/08/31
TI  - Integrating LSTM and Clustering SVM for Enhanced Stock Price Prediction
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 643
EP  - 652
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_64
DO  - 10.2991/978-94-6463-823-3_64
ID  - Li2025
ER  -