The Role of Time Series Analysis in Stock Market Prediction

DOI

10.2991/978-94-6463-598-0_34How to use a DOI?

Keywords

Time Series Analysis; Stock Market Prediction; ARIMA; GARCH; LSTM

Abstract

This study explores the application of time series analysis in predicting stock market trends, focusing on the ARIMA (AutoRegressive Integrated Moving Average), GARCH (Generalized Autoregressive Conditional Heteroskedasticity), and LSTM (Long Short-Term Memory) models. These models have been selected for their unique capabilities in capturing different aspects of market behavior, from linear trends to volatility clustering and complex temporal dependencies. Through a comprehensive literature review and comparative case study analysis, this research evaluates the effectiveness of these models in various market environments, particularly in emerging markets. The findings suggest that while classical models like ARIMA and GARCH are effective for short-term predictions, integrating them with modern machine learning techniques such as LSTM can significantly enhance prediction accuracy and robustness. This study contributes to the ongoing development of more sophisticated forecasting tools, offering practical insights for investors and financial analysts in optimizing their decision-making processes.

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  - Jiali Shi
PY  - 2024
DA  - 2024/12/19
TI  - The Role of Time Series Analysis in Stock Market Prediction
BT  - Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024)
PB  - Atlantis Press
SP  - 329
EP  - 334
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-598-0_34
DO  - 10.2991/978-94-6463-598-0_34
ID  - Shi2024
ER  -