Temporal Convolutional Network

Temporal Convolutional Networks (TCNs) are a class of deep learning models designed for processing sequential data, particularly time series, by leveraging the power of convolutional operations across time. Current research focuses on enhancing TCNs through integration with other architectures like Transformers and attention mechanisms to improve performance in various applications, including time series forecasting, action recognition, and anomaly detection. This work is significant because TCNs offer a powerful and efficient approach to analyzing temporal data, leading to advancements in diverse fields ranging from environmental monitoring (e.g., wind speed forecasting) to healthcare (e.g., emotion recognition from physiological signals).