Recurrent Neural Networks - Architectures and Applications: Analyzing architectures and applications of recurrent neural networks (RNNs) for modeling sequential data and time-series prediction

Abstract

Recurrent Neural Networks (RNNs) have emerged as a powerful tool for modeling sequential data and time-series prediction due to their ability to capture temporal dependencies. This paper provides a comprehensive analysis of the architectures and applications of RNNs in various domains. We begin by discussing the basic architecture of RNNs and then delve into more advanced variants, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), highlighting their differences and advantages.

Next, we explore the diverse applications of RNNs, including natural language processing (NLP), speech recognition, machine translation, and time-series forecasting. We discuss how RNNs are used in each application, their performance, and challenges faced in real-world scenarios. Additionally, we review recent developments and trends in RNN research, such as attention mechanisms and transformer-based architectures, which have further improved the capabilities of RNNs.

Finally, we conclude with a discussion on future directions and potential research avenues for advancing RNN architectures and applications.