Advances in Time Series Forecasting with Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 162
Special Issue Editors
Interests: time series analysis and forecasting; machine learning; explainable AI
Special Issue Information
Dear Colleagues,
Time series forecasting plays a pivotal role in diverse fields, including finance, healthcare, energy, climate science, and supply chain management. With the rapid advancements in machine learning, deep learning, and statistical modeling, forecasting accuracy and efficiency have reached unprecedented levels. This Special Issue aims to explore cutting-edge methodologies, innovative algorithms, and practical applications in time series forecasting, fostering interdisciplinary research and real-world impact.
We invite contributions addressing key challenges such as handling high-dimensional data, improving interpretability, integrating domain knowledge, and adapting to non-stationary environments. Topics of interest include (but are not limited to) neural forecasting architectures, probabilistic forecasting, hybrid models, explainable AI for time series, and large-scale forecasting systems. Additionally, we encourage submissions showcasing novel applications in emerging domains like renewable energy prediction, epidemiological modeling, and industrial IoT.
This Special Issue seeks to bridge the gap between theoretical advancements and practical implementations, providing a platform for researchers and practitioners to share insights, benchmark techniques, and discuss future directions. By compiling state-of-the-art research, we aim to accelerate progress in time series forecasting and highlight its transformative potential across industries.
Dr. Waddah Saeed
Dr. Eufrásio De Andrade Lima Neto
Guest Editors
Manuscript Submission Information
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Keywords
- time series forecasting
- machine learning for forecasting
- deep learning for forecasting
- conformal prediction in forecasting
- forecast explainability
- data-driven forecasting
- real-world forecasting applications
- hybrid forecasting models
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