Time series forecasting in financial markets using deep learning models

Abstract

This research paper explores the application of deep learning models in time series forecasting for financial markets. We investigate the performance of various deep learning architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer models, in predicting stock prices and market trends. The study compares these advanced techniques with traditional statistical methods and evaluates their effectiveness in capturing complex patterns and dependencies in financial time series data. Our findings demonstrate the superior predictive capabilities of deep learning models, particularly in handling non-linear relationships and long-term dependencies. The research also highlights the importance of feature engineering, model selection, and hyperparameter tuning in achieving accurate forecasts. The results provide valuable insights for researchers and practitioners in the field of financial forecasting and contribute to the ongoing development of more robust and reliable predictive models for financial markets.