5 days ago · Temporal Convolutional Network — An Overview · Key Problem Domain: The real magic of TCNs lies in how they manage sequence modeling and time-series forecasting.

3 days ago · Temporal Convolutional Networks (TCNs) are a specialized type of convolutional neural network designed for time series data. TCNs use 1D dilated convolutions to ...

4 days ago · To find an accurate open-set gas classification model, we proposed a MSE-TCN, which integrates squeeze-and-excitation residual network (SE-ResNet) internally ...

18 hours ago · In this paper, we propose Temporal Convolutional Networks and Kernel Extreme Learning Machine DDPG (TCN-KELM-DDPG) energy management strategy of HEV based on ...

6 days ago · The improved temporal convolutional network (ITCN) represents an extension of the traditional TCN. In comparison to the traditional TCN, the ITCN exhibits ...

4 days ago · Our work introduces a framework that combines temporal relational graph convolutional networks with financial performance prediction, offering a way to ...

3 days ago · In response, we introduce a promising solution: the Temporal Fusion Graph Convolutional Network. This innovative approach aims to rectify the inadequate ...

3 days ago · Kaestner et al. report that convolutional neural networks using whole brain three-dimensional T1-weighted scans can detect the neural signature of epilepsy.

4 days ago · Given its effective ability to model the spatial–temporal dependencies of road networks, the GCN demonstrates superior performance for traffic flow forecasting, ...

7 days ago · This survey provides a structured and comprehensive overview of state-of-the-art deep learning for time series anomaly detection.