Recurrent Neural Networks for Time Series Forecasting

Dear Colleagues,

Recurrent neural networks (RNN) are a special family of neural networks that are designed for sequential data such as time series forecasting or natural language processing. RNN architectures are one of the major deep learning algorithms that are able to perform temporal analysis due to their internal memory, which stores information from the past. RNNs have seen several advancements, such as long short-term memory (LSTM), gated recurrent units (GRU), and convolutional LSTM (ConvLSTM), among others. Nevertheless, RNNs are still facing challenges in training costs, generalizability, performance with noisy data, and architecture optimization. This Special Issue invites researchers to present high-quality studies on RNN techniques for time series forecasting, ranging from theoretical studies to the development of innovative applications. Potential topics of interest include but are not limited to:

• Novel algorithms exploring advancements in LSTM and GRU for time series forecasting.
• Novel algorithms that combine RNN with advancements in machine learning such as bi-directional algorithms, attention mechanisms, transformers, and multiplicative LSTM for time series forecasting.
• Novel algorithms that explore ConvLSTM architectures for spatio-temporal forecasting.
• Novel algorithms allowing the use of evolutionary computation to optimize RNN weights and hyperparameters in forecasting.
• Novel algorithms for time series forecasting under uncertainty with the use of Gaussian processes or Bayesian techniques.
• Novel algorithms for time series forecasting with limited data.
• Parallel and distributed RNN algorithms to accelerate training on GPUs.
• Exploration of RNN autoencoders and/or transformers for time series anomaly detection.
• Engineering applications of RNN algorithms in all disciplines are welcomed, while applications in carbon-free energy are particularly encouraged.

Dr. Majdi I. Radaideh
Dr. Xingang Zhao
Dr. Yifeng Che
Dr. Mohammad G. Abdo
Guest Editors

Manuscript Submission Information

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