Submission Deadline: 31 August 2025 View: 167 Submit to Special Issue
Prof. Dr. Shiping Wen
Email: shiping.wen@uts.edu.au
Affiliation: Australian AI Institute, University of Technology Sydney, Australia
Homepage: Shiping Wen Profile | University of Technology Sydney
Research Interests: Neural Network, Computer Vision, Safety-critical Control, Memristor
Prof. Dr. Yao Chen
Email: chenyao@swufe.edu.cn
Affiliation: Department of Computer Science, Southwestern University of Finance and Economics, China
Homepage: www.yaolab.cn
Research Interests: Distributed Computing, Reinforcement Learning, Complex Networks
In recent years, deep learning has emerged as a pivotal breakthrough in the field of time series forecasting within artificial intelligence (AI), leading numerous technological revolutions and increasingly permeating scientific domains such as finance, environmental science, health monitoring, and energy management. These advanced models, leveraging vast datasets and complex algorithms, not only enhance the precision of data analysis but also offer innovative solutions to intricate scientific and engineering challenges.
From theoretical exploration to practical application, deep learning-based models for time series forecasting have demonstrated unique advantages in simulating and predicting various physical phenomena and complex processes. In the financial sector, deep learning is utilized to analyze and predict market trends; in environmental science, it aids in forecasting climate changes and environmental pollution; in health monitoring, deep learning techniques are crucial for identifying disease patterns and predicting health trends. With the rapid development of AI technologies, the way we explore the world is undergoing a fundamental transformation.
This special issue is dedicated to showcasing the latest advancements and future directions of deep learning in the realm of time series forecasting. We invite researchers worldwide to submit original research papers, reviews, and technical reports that explore innovative applications, challenges, and solutions related to deep learning. Anticipated topics include, but are not limited to, the following areas:
● Time series anomaly detection for industrial applications.
● Applications of time series forecasting in climate modeling, environmental monitoring, and renewable energy.
● Applications of time series forecasting in data-driven science.
● Time series forecasting applications in biomedical and healthcare sectors.
● Development of general-purpose time series forecasting algorithms and computational frameworks.
● Interpretability and robustness studies of time series forecasting models.
● Security and privacy protection in deep learning models.
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