Time Transformer

Time Transformers are a class of neural network architectures designed to effectively process sequential data, particularly time series, by leveraging the attention mechanisms inherent in transformer models. Current research focuses on adapting and extending these architectures for diverse applications, including time series forecasting, action recognition, and audio/video generation, often incorporating innovations like joint-axis attention, continuous-time modeling, and efficient attention mechanisms to improve performance and scalability. These advancements are significantly impacting various fields by enabling more accurate predictions, improved data generation capabilities, and more efficient processing of complex temporal data.