2024/08/22 by Gonzalo Lopez Gil, Gil, Gonzalo Lopez, Paul Duhamel-Sebline +3 · 2 voices · 1 citation
Computer Science · Decision Sciences · #FOS: Computer and information sciences #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Stock Market Forecasting Methods #Time Series Analysis and Forecasting #cs.LG
paper · pdf · doi:10.48550/arxiv.2408.12408
openalex publication_date 2024/08/22 · arxiv published 2024/08/22 · arxiv updated 2024/08/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The stock market is a fundamental component of financial systems, reflecting economic health, providing investment opportunities, and influencing global dynamics. Accurate stock market predictions can lead to significant gains and promote better investment decisions. However, predicting stock market trends is challenging due to their non-linear and stochastic nature. This study investigates the efficacy of advanced deep learning models for short-term trend forecasting using daily and hourly closing prices from the S&P 500 index and the Brazilian ETF EWZ. The models explored include Temporal Convolutional Networks (TCN), Neural Basis Expansion Analysis for Time Series Forecasting (N-BEATS), Temporal Fusion Transformers (TFT), Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS), and Time-series Dense Encoder (TiDE). Furthermore, we introduce the Extended Long Short-Term Memory for Time Series (xLSTM-TS) model, an xLSTM adaptation optimised for time series prediction. Wavelet denoising techniques were applied to smooth the signal and reduce minor fluctuations, providing cleaner data as input for all approaches. Denoising significantly improved performance in predicting stock price direction. Among the models tested, xLSTM-TS consistently outperformed others. For example, it achieved a test accuracy of 72.82% and an F1 score of 73.16% on the EWZ daily dataset. By leveraging advanced deep learning models and effective data preprocessing techniques, this research provides valuable insights into the application of machine learning for market movement forecasting, highlighting both the potential and the challenges involved.