Stock Price Prediction Based on ARIMA-LSTM Model: A Case Study of the Education Sector

Zihan Wang

doi:10.47297/wspceWSP2516-251921.20250903

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Stock Price Prediction Based on ARIMA-LSTM Model: A Case Study of the Education Sector

Volume 9, Issue 3

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Stock Price Prediction Based on ARIMA-LSTM Model: A Case Study of the Education Sector
Stock Price Prediction Based on ARIMA-LSTM Model: A Case Study of the Education Sector
Department of Financial Mathematics, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, 310000, China
Vol. 9, Issue 3, Pages: 106-111(2025)
Published：30 June 2025
DOI：10.47297/wspceWSP2516-251921.20250903
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Wang Zihan.Stock Price Prediction Based on ARIMA-LSTM Model: A Case Study of the Education Sector[J].Creative Economy,2025,09(03):106-111.
DOI：

Wang Zihan.Stock Price Prediction Based on ARIMA-LSTM Model: A Case Study of the Education Sector[J].Creative Economy,2025,09(03):106-111. DOI： 10.47297/wspceWSP2516-251921.20250903.

摘要

Abstract

In recent years

frequent reforms in education policies and the national emphasis on education-powered nation-building have led to significant fluctuations in the development of the education sector. Accurate prediction of financial time series has become a critical focus for investors. This study proposes a novel ARIMA-LSTM hybrid model to predict stock prices of the education sector index

Chuanzhi Education

and Guomai Media

and compares its performance with baseline ARIMA and LSTM models to evaluate predictive accuracy. Empirical results demonstrate that the ARIMA-LSTM model achieves superior accuracy in stock price forecasting compared to standalone ARIMA and LSTM models. By forecasting the development trends of the education sector

this study reveals that its future trajectory will remain relatively stable.

关键词

Keywords

references

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