Recurrent Neural Networks (RNNs) for Time-Series Data: Applications and Advancement

Abstract

Recurrent Neural Networks (RNNs) have emerged as a powerful class of neural
networks for modeling sequential and time-series data. Their unique ability to retain information
over time through internal states makes them suitable for various applications such as speech
recognition, stock market prediction, and climate modeling. This paper explores the theoretical
foundation of RNNs, their strengths, limitations, and prominent applications in time-series
forecasting. A detailed review of pre-2013 literature highlights the advancements in architectures
and learning algorithms. Empirical results demonstrate the effectiveness of RNNs in predicting
real-world time-series datasets.

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