Temporal Convolutional Networks - Architectures and Applications: Investigating temporal convolutional networks (TCNs) and their applications in modeling sequential data with long-range dependencies

Abstract

Temporal Convolutional Networks (TCNs) have emerged as a powerful class of models for processing sequential data, offering advantages over traditional recurrent neural networks (RNNs) such as long short-term memory (LSTM) networks. TCNs utilize one-dimensional convolutions to capture temporal dependencies in the data, enabling them to model long-range dependencies more effectively. This paper provides an in-depth overview of TCNs, including their architecture, training, and key properties. We also discuss various applications of TCNs across different domains, highlighting their effectiveness in tasks such as speech recognition, natural language processing, and time series forecasting. Finally, we discuss current challenges and future directions for TCN research, including potential improvements in architecture and training algorithms.