The Analysis of Hidden Units in LSTM Model for Accurate Stock Price Prediction

Menghao Deng

2023

Abstract

Stock market forecasting has always been difficult due to its complicated and volatile character. Deep learning approaches have demonstrated promising results in a variety of domains, including stock market prediction, in recent years. This research introduces Long Short-Term Memory (LSTM) for forecast of stock market and examines the impact of model’s hidden units. The LSTM model is developed on historical stock market data to find intricate patterns and linkages. Technical indicators and sentiment analysis can also be used as potential input variables to improve the model’s predictive capacity. The suggested model is tested against a large dataset of stock market values and compared to established algorithms of deep learning. The purpose of this research is to investigate the role and implementation of hidden units in LSTM networks. These hidden units learn long-term dependencies and recall them in a way that many other forms of Recurrent Neural Networks (RNN) do not. Investigations in paper show that changing the number of hidden units has an effect on prediction results. The findings of this work add to the expanding corpus of research on using deep learning techniques for stock market forecasting and analysis, with potential implications in financial decision-making and risk management.

Paper Citation

in Harvard Style

Deng M. (2023). The Analysis of Hidden Units in LSTM Model for Accurate Stock Price Prediction. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 411-416. DOI: 10.5220/0012799100003885

in Bibtex Style

@conference{daml23,
author={Menghao Deng},
title={The Analysis of Hidden Units in LSTM Model for Accurate Stock Price Prediction},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={411-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012799100003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - The Analysis of Hidden Units in LSTM Model for Accurate Stock Price Prediction
SN - 978-989-758-705-4
AU - Deng M.
PY - 2023
SP - 411
EP - 416
DO - 10.5220/0012799100003885
PB - SciTePress