Fan, X.; Tao, C.; Zhao, J. Advanced Stock Price Prediction with xLSTM-Based Models: Improving Long-Term Forecasting. Preprints2024, 2024082109. https://doi.org/10.20944/preprints202408.2109.v1
APA Style
Fan, X., Tao, C., & Zhao, J. (2024). Advanced Stock Price Prediction with xLSTM-Based Models: Improving Long-Term Forecasting. Preprints. https://doi.org/10.20944/preprints202408.2109.v1
Chicago/Turabian Style
Fan, X., Chunliang Tao and Jianyu Zhao. 2024 "Advanced Stock Price Prediction with xLSTM-Based Models: Improving Long-Term Forecasting" Preprints. https://doi.org/10.20944/preprints202408.2109.v1
Abstract
Stock price prediction has long been a critical area of research in financial modeling. The inherent complexity of financial markets, characterized by both short-term fluctuations and long-term trends, poses significant challenges in accurately capturing underlying patterns. While Long Short-Term Memory (LSTM) networks have shown strong performance in short-term stock price prediction, they struggle with effectively modeling long-term dependencies. In this paper, we propose an advanced stock price prediction model based on the Extended Long Short-Term Memory (xLSTM) algorithm, designed to enhance predictive accuracy over both short and long-term periods. We conduct extensive experiments by building and evaluating models based on xLSTM and LSTM architectures for multiple stocks. Our results demonstrate that the xLSTM model consistently outperforms the LSTM model across all stocks and time horizons, with the performance gap widening as the prediction period extends. The observations underscore the superior capability of the xLSTM-based model to capture long-term patterns in financial data, offering a promising approach for more accurate and reliable stock price predictions.
Keywords
Neural Networks; Stock Price Prediction; Long Short-Term Memory (LSTM); Extended Long Short-Term Memory (xLSTM); Time Series Forecasting
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
