Time Series Forecasting with Recurrent Neural Networks: An In-depth Analysis and Comparative Study

Abstract

Time series forecasting is a critical aspect of data-driven decision-making in various domains such as finance, healthcare, and weather prediction. In recent years, Recurrent Neural Networks (RNNs) have emerged as powerful tools for capturing temporal dependencies in sequential data, making them well-suited for time series forecasting tasks. This paper presents an in-depth analysis and a comparative study of the effectiveness of RNNs in time series forecasting. The study begins by providing a comprehensive review of the existing literature on time series forecasting methods, highlighting the strengths and limitations of traditional techniques. Subsequently, the architecture and functioning of RNNs are explored, emphasizing their ability to model long-range dependencies through recurrent connections. To evaluate the performance of RNNs in time series forecasting, we conduct experiments on diverse datasets representing different domains and characteristics. We compare the results with those obtained from traditional time series forecasting methods, including autoregressive models and moving averages.