Temporal Feature

Temporal features, encompassing the sequential and dynamic aspects of data across time, are crucial for understanding various phenomena in diverse fields. Current research focuses on effectively integrating temporal information into models, employing architectures like transformers, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), often combined with attention mechanisms to capture long-range dependencies and improve efficiency. This work is significant because accurate modeling of temporal dynamics is essential for advancements in areas such as video generation, autonomous driving, and anomaly detection, leading to improved performance and interpretability in these applications.