Comparative Analysis of Deep Learning Algorithms for Predicting Financial Market Time Series

This study presents a comprehensive comparative analysis of five prominent deep learning algorithms for predicting financial market time series: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), Transformer, and DeepAR. Using a diverse dataset of stock prices from the S&P 500 index, we evaluate these algorithms based on their predictive accuracy, computational efficiency, and robustness to market volatility. Our results indicate that while all models demonstrate strong predictive capabilities, the Transformer and TCN models consistently outperform others in terms of accuracy and handling of long-term dependencies. The LSTM and GRU models show comparable performance with faster training times, while DeepAR exhibits strong performance in volatile market conditions. This analysis provides valuable insights for researchers and practitioners in selecting appropriate deep learning models for financial time series forecasting tasks.