Special Issue "Recurrent Neural Networks for Time Series Forecasting"
Deadline for manuscript submissions: closed (1 January 2023) | Viewed by 3444
Interests: machine learning; time series forecasting; uncertainty quantification; optimization; surrogate modeling; multiphysics simulations; nuclear power engineering; autonomous control
Interests: artificial intelligence; machine learning; time series forecasting; decision science; digital engineering; clean energy; nuclear engineering; modeling and simulation
Interests: machine Learning; uncertainty quantification; time series forecasting; statistical inference; reduced-order modeling; nuclear engineering
Interests: machine learning; deep learning; time series analysis; digital twins; reduced order modeling; uncertainty quantification; sensitivity analysis; metaheuristic optimization; transformer models; attention mechanisms
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- recurrent neural networks
- long short-term memory (LSTM)
- gated recurrent units (GRU)
- fault prognostics
- anomaly detection
- time series prediction with uncertainty
- multivariate forecasting
- signal processing
- forecasting in energy