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 January 2027 | Viewed by 1186
Special Issue Editors
Interests: time series analysis and forecasting; machine learning; explainable AI
Special Issues, Collections and Topics in MDPI journals
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
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 250 words) can be sent to the Editorial Office for assessment.
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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.
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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