Temporal Fusion Transformer

Temporal Fusion Transformers (TFTs) are a class of deep learning models designed for accurate and interpretable time series forecasting, particularly in multivariate settings with complex temporal dependencies. Current research focuses on applying TFTs and related architectures, such as those incorporating attention mechanisms and recurrent networks, to diverse domains including healthcare, aviation, energy management, and environmental science. These applications aim to improve prediction accuracy and provide valuable insights into underlying processes, leading to better decision-making and resource allocation in various fields. The ability of TFTs to handle both static and dynamic features, along with their inherent interpretability, makes them a powerful tool for time series analysis across numerous scientific and practical applications.