Vol. 8, Special Issue 3 (2019)

The survey commences with a nuanced examination of the fundamental principles underlying time series forecasting, elucidating the challenges posed by the inherent complexity and non-linearity of temporal data. Subsequently, the evolution of RNNs is meticulously traced, from the conventional architectures to the latest variants such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). The inherent capacity of RNNs to capture sequential dependencies is explicated, establishing the theoretical foundation for their widespread application in time series forecasting.

A critical facet of this survey is the detailed analysis of the state-of-the-art methodologies employed in RNN-based time series forecasting. This encompasses an exploration of hybrid models integrating RNNs with other deep learning architectures, as well as the integration of attention mechanisms and ensembling techniques to enhance predictive accuracy. The survey also scrutinizes the incorporation of domain-specific knowledge and the adaptation of transfer learning strategies to further refine forecasting outcomes.

Furthermore, the survey extends its purview to real-world applications where RNNs have demonstrated exceptional efficacy, including finance, healthcare, energy, and climate prediction. A discerning examination of these applications underscores the versatility and practical utility of RNNs in diverse domains.

As a testament to the dynamic nature of the field, the survey concludes with a forward-looking perspective, highlighting potential avenues for future research and the integration of emerging technologies such as explainable AI and meta-learning into the realm of time series forecasting.