Temporal Convolutional Network and its Application in Various Sectors

Artificial Intelligence has been working to meet the needs of humans for a very long time. There have been many progressive evolutions along the way. Neural Networks have emerged to be the best in the field of recognition and data analysis. There have been myriad developments and preparation of models over decades. There is CNN, RNN, FNN, DNN, TCN and many more. In this paper, we aim to compare the working of TCN which stands for Temporal Convolutional Network with the workings of above-mentioned models to figure out the best use of technology for catering the human needs. This paper is a deep analysis of the erstwhile models that are being used and how the temporal convolutional network finds its applications in the present scenario, what are the emerging fields that it can be implemented in and what were the positives of TCN over other neural network models. Through a careful study we have come to a conclusion that TCN could be the basic starting point of all the neural networks as it has proven to have less training time, less memory utilization and gives better results compared to other models. The functioning of TCN models is designed in such a way that no information can be drained from future to the past. Furthermore, the input image can be analyzed irrespective of the length as the same can be mapped to the output layer. TCN finds its application in various multilateral domains that includes healthcare, sequential forecasting, preventing cyber attacks, speech enhancement and many more. It has the capability to outperform the other models most of the time by a major difference and sometimes by a significant margin. Therefore, the applications of TCN can vary in accordance to the needs of the user.