Stock Prediction Based on Traditional Statistical Models, Machine Learning Models and Fusion Models

Yu Du

2024

Abstract

This study aims to evaluate how well machine learning (ML) algorithms and classic time series analysis methods can forecast stock market trends. Accurate forecasts of stock prices can greatly aid professionals and investors in making strategic decisions owing to the unpredictable nature of the stock market. This research aims to create a composite model that combines the accuracy of traditional statistical models, which are good at making short-term predictions, with the capabilities of machine learning models that can handle large amounts of complex and nonlinear data. The goal is to enhance the precision of long-term stock price forecasts. This research aims to assess the strengths and weaknesses of four distinct models: Autoregressive Integrated Moving Average(ARIMA), Generalized Autoregressive Conditional Heteroskedasticity(GARCH), Long Short-Term Memory (LSTM), and Random Forest(RF) through training and evaluation with historical stock market data. Additionally, a comparison between these distinct models and an integrated model will be conducted as part of the investigation to develop a more reliable tool for informing investment decisions.

Paper Citation

in Harvard Style

Du Y. (2024). Stock Prediction Based on Traditional Statistical Models, Machine Learning Models and Fusion Models. 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 160-168. DOI: 10.5220/0013007100004601

in Bibtex Style

@conference{iampa24,
author={Yu Du},
title={Stock Prediction Based on Traditional Statistical Models, Machine Learning Models and Fusion Models},
booktitle={Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA},
year={2024},
pages={160-168},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013007100004601},
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 Prediction Based on Traditional Statistical Models, Machine Learning Models and Fusion Models
SN - 978-989-758-722-1
AU - Du Y.
PY - 2024
SP - 160
EP - 168
DO - 10.5220/0013007100004601
PB - SciTePress