Time Series Forecasting with Recurrent Neural Networks: Exploring the use of recurrent neural networks (RNNs) for time series forecasting in various domains

Abstract

Time series forecasting plays a crucial role in various domains, including finance, weather prediction, and resource management. Recurrent Neural Networks (RNNs) have shown remarkable performance in modeling sequential data, making them suitable for time series forecasting tasks. This paper provides a comprehensive overview of the use of RNNs for time series forecasting, covering various architectures, training techniques, and applications. We analyze the strengths and limitations of RNNs in handling different types of time series data and compare them with traditional forecasting methods. Additionally, we discuss recent advancements, challenges, and future directions in RNN-based time series forecasting research. Overall, this paper serves as a valuable resource for researchers and practitioners interested in leveraging RNNs for time series forecasting tasks.