Comparative Analysis of Stock Price Prediction Based on ARIMA and LSTM Models

DOI

10.2991/978-94-6463-835-6_3How to use a DOI?

Keywords

Time series analysis; stock price prediction; ARIMA model; LSTM model; hybrid model

Abstract

This study delves into the application of the Autoregressive Integrated Moving Average Model (ARIMA), Long Short-Term Memory Network (LSTM), and their hybrid models of stock price prediction. It offers a comparative analysis of these models’ performances, highlighting the significance of accurate forecasting in the volatile world of stock markets. The paper begins by underscoring the importance of stock market forecasting and the limitations of traditional time series analysis techniques, particularly the ARIMA model’s struggle with long-term dependencies and nonlinear features. It then explores the LSTM model’s prowess in capturing long-term patterns within time series data and the potential of hybrid models that marry the strengths of both LSTM and ARIMA to enhance forecasting precision. Through case analysis, the research evaluates these models’ performance across different market environments, culminating in comprehensive recommendations for model selection. The study concludes that hybrid models effectively integrate the strengths of ARIMA and LSTM, significantly boosting the accuracy and robustness of time series forecasting. It advocates for future research to explore a range of datasets to affirm the model’s universality and robustness and to probe the impact of varying data characteristics on predictive power, thereby offering more targeted guidance for practical applications.

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.

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TY  - CONF
AU  - Qi Wu
PY  - 2025
DA  - 2025/09/17
TI  - Comparative Analysis of Stock Price Prediction Based on ARIMA and LSTM Models
BT  - Proceedings of the 2025 3rd International Academic Conference on Management Innovation and Economic Development (MIED 2025)
PB  - Atlantis Press
SP  - 14
EP  - 22
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-835-6_3
DO  - 10.2991/978-94-6463-835-6_3
ID  - Wu2025
ER  -