Stock Price Prediction Based on CNN, LSTM and CNN- LSTM Model

Yufei Wang

2024

Abstract

Stock investments are perennially recognized for their high potential returns and commensurate risk levels. While CNN and LSTM models individually demonstrate proficiency in data prediction, each has its inherent limitations. In pursuit of overcoming these limitations, this research proposes a composite CNN-LSTM model. Initially, this paper selects two disparate stocks for evaluation, employing the individual CNN and LSTM models to predict their prices. Subsequently, the construction of the hybrid model involves utilizing the CNN layers to extract spatial features, which are then transformed into a one-dimensional vector. This vector is subsequently fed into the LSTM layer to capitalize on its sequence data handling capabilities, culminating in the model's predictive execution. The final phase of this study entails a comparative analysis of the predictive performance. The results show that the CNN-LSTM model inherits the advantages of the two individual models and is both highly stable and extremely efficient in making predictions on big stock data. This enhancement is particularly notable when compared to the single CNN and LSTM models, underscoring the efficacy of integrating these two distinct computational approaches into a unified predictive framework.

Paper Citation

in Harvard Style

Wang Y. (2024). Stock Price Prediction Based on CNN, LSTM and CNN- LSTM Model. In Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA; ISBN 978-989-758-722-1, SciTePress, pages 22-30. DOI: 10.5220/0012982600004601

in Bibtex Style

@conference{iampa24,
author={Yufei Wang},
title={Stock Price Prediction Based on CNN, LSTM and CNN- LSTM Model},
booktitle={Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA},
year={2024},
pages={22-30},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012982600004601},
isbn={978-989-758-722-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA
TI - Stock Price Prediction Based on CNN, LSTM and CNN- LSTM Model
SN - 978-989-758-722-1
AU - Wang Y.
PY - 2024
SP - 22
EP - 30
DO - 10.5220/0012982600004601
PB - SciTePress