The State-of-the-Art Price Prediction Scenarios: From Time Series Methods to Deep Learning

Yulin Tang

2024

Abstract

As a matter of fact, stock price prediction is always one of the most challenge topics in finance fields. Contemporarily, thanks to the rapid development of computer science, the prediction approaches have been upgraded from time series models to deep learning scenarios. With this in mind, this study explores the latest research progress in stock price prediction, particularly the application of time series analysis and deep learning in this field. By comparing the autoregressive integrated moving average (ARIMA) model with the long short-term memory (LSTM) model, this study analyses advantages and limitations of both models in stock price prediction. Based on the evaluations, the research results show that the ARIMA model performs stably in short-term prediction, while the LSTM model demonstrates strong long-term prediction ability in complex market environments. At the same time, this study also discusses the challenges facing stock price prediction and looks forward to the research direction of integrating multiple models to improve prediction accuracy in the future.

Paper Citation

in Harvard Style

Tang Y. (2024). The State-of-the-Art Price Prediction Scenarios: From Time Series Methods to Deep Learning. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 117-122. DOI: 10.5220/0013207500004568

in Bibtex Style

@conference{ecai24,
author={Yulin Tang},
title={The State-of-the-Art Price Prediction Scenarios: From Time Series Methods to Deep Learning},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={117-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013207500004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - The State-of-the-Art Price Prediction Scenarios: From Time Series Methods to Deep Learning
SN - 978-989-758-726-9
AU - Tang Y.
PY - 2024
SP - 117
EP - 122
DO - 10.5220/0013207500004568
PB - SciTePress