In recent years, deep learning models have gained significant traction for time series forecasting due to their ability to capture complex temporal dependencies. Among these models, Long Short-Term Memory (LSTM) networks and Transformer deep learning models have emerged as leading approaches. This chapter provides a comprehensive review of LSTM and Transformer models in the context of time series forecasting. It explores their theoretical underpinnings, highlights advancements, and discusses their applications across various domains. Through an analysis of existing literature, it compares the strengths and limitations of each model, providing insights into their performance in different forecasting scenarios. This chapter aims to guide researchers and practitioners in selecting the appropriate model for specific time series financial analysis and forecasting tasks, considering the unique characteristics of their datasets and the desired forecasting horizon.