Stock Price Prediction Based on Machine Learning

DOI

10.2991/978-94-6463-540-9_11How to use a DOI?

Keywords

machine learning; LSTM model; ARIMA model; stock price prediction

Abstract

Due to the rapid development of the internet and the financial industry, stock price prediction has become a widely discussed topic. Government departments and regulatory agencies use stock price forecasts to understand the market’s health, which helps formulate economic strategies and prevent systemic financial risks. By predicting their stock price fluctuations and those of their competitors, companies can better allocate resources and make decisions for the future. For financial academia and professional analysts, stock price forecasting is an important tool for studying market behavior and economic trends. Based on existing machine learning research, this paper aims to analyze the effectiveness of the Long-Short Term Memory (LSTM) networks and the Autoregressive Integrated Moving Average (ARIMA) model in predicting Apple’s stock price comparatively. Additionally, it seeks to summarize the advantages and disadvantages of each model. The experimental results indicate that the LSTM model fits the test set more closely to the actual values, and its Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) values are all relatively smaller. This suggests that the LSTM model exhibits slightly better accuracy and evaluation metrics for long-term predictions than the ARIMA model. Overall, in the research on improving long-term and large-scale prediction results, scholars can focus more on optimizing and enhancing LSTM models in the future compared to ARIMA models. In contrast, improving the accuracy and credibility of LSTM model predictions has more potential for application and academic value.

Copyright

© 2024 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  - Ke Huang
PY  - 2024
DA  - 2024/10/16
TI  - Stock Price Prediction Based on Machine Learning
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 87
EP  - 97
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_11
DO  - 10.2991/978-94-6463-540-9_11
ID  - Huang2024
ER  -