Work

Stock price prediction based on hybrid CNN-LSTM model

Year: 2024

Type: article

Abstract: Stock price prediction is of great significance but faces numerous challenges. In this study, a hybrid CNN-LSTM model was utilized. The stock data were processed and feature engineering was conducted. The model architecture and training strategies were expounded. Experimental results demonstrated that this model outperformed traditional methods and benchmark models on the test set, featuring strong capabilities in feature extraction and handling long-term dependencies. Key roles of data preprocessing, hyperparameter adjustment, and model fusion were also summarized, providing valuable references for stock price prediction.

Source: Applied and Computational Engineering

DOI: 10.54254/2755-2721/104/20241065

Authors: Qinghe Zhao, Yue Hao, Xuechen Li

Institutions: University of Exeter, Johns Hopkins University, New York University

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W4404249473

FWCI: 6.161

Cites:

Cited by: 20

Related to: 10