Deep Learning for Time-Series Forecasting
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 10 July 2026 | Viewed by 836
Special Issue Editors
Interests: artificial intelligence; foundation models; deep reinforcement learning; optimization systems; intelligent transport systems
Interests: artificial intelligence; knowledge engineering; internet of things
Special Issues, Collections and Topics in MDPI journals
Interests: software engineering; deep learning; artificial intelligence; cloud computing; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep learning (DL) offers a robust approach to time-series forecasting across diverse domains including energy, climate and environment, healthcare, mobility and transportation, tourism and smart cities more generally, as well as industry and manufacturing, finance, and economics.
Despite the popularity and importance of the integration of forecasting capabilities in decision support systems and decision-making processes, there are numerous challenges when dealing with deep learning for time-series forecasting. In the literature, model-centric studies often focus on complex black-box architectures that achieve high predictive accuracy, but the results need to be explained to be truly actionable. Data-centric studies, on the other hand, explore the importance of data quality and data management to enhance forecasting capabilities.
Recent research is also exploring foundational model approaches capable of predicting heterogeneous time series across multiple domains as well as solutions that exploit large language models (LLMs). However, in many cases, simpler DL models still outperform more general-purpose architectures when sufficient historical data are available.
This Special Issue seeks to highlight state-of-the-art methodologies, practical applications, and emerging directions in deep learning for time-series forecasting, emphasizing both methodological innovations and impactful real-world implementations.
Dr. Enrico Collini
Dr. Pierfrancesco Bellini
Prof. Dr. Haiping Xu
Guest Editors
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Keywords
- deep learning
- time-series forecasting
- foundational time-series forecasting
- explainable artificial intelligence
- big data
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