Advances in Artificial Intelligence for Stock Price Prediction: A Comprehensive Investigation of Models and Applications

Tianai Chen

2024

Abstract

Traditional prediction models in stock price prediction are inefficient due to market changes. Artificial Intelligence (AI) technologies have improved stock price forecast precision and adaptability. The steps in the machine learning process are gathering and preparing data, training and testing sets, random forest machine learning approaches, and deploying models for real-world applications. Regression and classification issues are handled by employing strategies such as multivariate linear regression, decision trees, and random forests to predict continuous target variables. Decision trees capture non-linear connections and are resistant to outliers. In order to solve the vanishing gradient issue and beat conventional Recurrent Neural Networks (RNNs) including audio recognition, time series forecasting, and handwriting identification, recurrent neural networks with Long Short-Term Memory (LSTM) are employed. Deep learning algorithms are increasingly replacing linear regression in AI stock price prediction due to complex nonlinear relationships in the market. These models can process large amounts of data for pattern recognition and feature extraction, increasing prediction accuracy. However, AI models face limitations such as not providing detailed explanations for patterns, not being efficient for different stocks, and not considering external effects. Some advanced methods such as expert systems and transfer learning could be considered to solve these limitations.

Paper Citation

in Harvard Style

Chen T. (2024). Advances in Artificial Intelligence for Stock Price Prediction: A Comprehensive Investigation of Models and Applications. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 51-55. DOI: 10.5220/0013487200004619

in Bibtex Style

@conference{daml24,
author={Tianai Chen},
title={Advances in Artificial Intelligence for Stock Price Prediction: A Comprehensive Investigation of Models and Applications},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={51-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013487200004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Advances in Artificial Intelligence for Stock Price Prediction: A Comprehensive Investigation of Models and Applications
SN - 978-989-758-754-2
AU - Chen T.
PY - 2024
SP - 51
EP - 55
DO - 10.5220/0013487200004619
PB - SciTePress