A Hybrid LSTM-Transformer Approach for Financial Markets: Forecasting Stock Price Time Series

Although the most used Long Short-Term Memory (LSTM) networks are now well-suited for short-term data changes, they are not enough to grasp the overall data in financial forecasting. This paper proposes a hybrid approach that combines LSTM and Transformer architectures to leverage both temporal dynamics and global dependencies in financial time series. Specifically, the model employs LSTM layers to extract sequential and local temporal features, while the Transformer’s self-attention mechanism c