In-Depth Review of Neural Network Architectures for Forecasting Heart Rate Time Series Data

The accurate prediction of heart rate is critical for the proactive monitoring and management of cardiovascular health, a leading concern worldwide due to the prevalence of cardiovascular diseases. Traditional time series forecasting methods, such as ARIMA and Prophet, often fall short in addressing the complex, non-linear nature of heart rate data, which is inherently noisy and highly variable. This paper provides a comprehensive review of contemporary neural network architectures that have shown promise in this domain, specifically focusing on Long Short-Term Memory (LSTM) networks, transformer-based models (PatchTST and iTransformer), Tiny Time Mixers (TTMs), MOMENT models, and deep reinforcement learning. We delve into the architectural intricacies of these models, their training processes, and the performance metrics used to evaluate them. Our analysis highlights the unique strengths and limitations of each approach, emphasizing their suitability for heart rate time series forecasting. Through empirical evidence and comparative analysis, we demonstrate that transformer-based models, TTMs, MOMENT models and deep reinforcement learning significantly enhance forecasting accuracy and efficiency over traditional methods. This review aims to provide a detailed understanding of these advanced techniques, offering valuable insights for future research and practical applications in the field of cardiovascular health monitoring.