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Mathematical and Statistical Methods for Prediction and Optimisation in Artificial...
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Mathematical and Statistical Methods for Prediction and Optimisation in Artificial Intelligence

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 417

Special Issue Editor

Prof. Dr. Diwei Zhou

E-Mail Website
Guest Editor
Department of Mathematical Sciences, School of Science, Loughborough University, Loughborough LE11 3TU, UK
Interests: applied statistics; machine learning; data science; predictive modelling

Special Issue Information

Dear Colleagues,

This Special Issue aims to showcase recent developments in mathematical and statistical methods that support prediction and optimisation within artificial intelligence (AI). As AI continues to transform sectors such as healthcare, transport, energy, finance, and environmental science, there is a growing demand for robust theoretical foundations and efficient computational techniques.

We welcome original research and review articles that advance mathematical modelling, statistical inference, and optimisation techniques with relevance to AI. Contributions may focus on novel theoretical insights, methodological innovations, or practical applications where mathematical and statistical principles enhance the performance, interpretability, or trustworthiness of AI systems.

We particularly encourage submissions that explore the intersection of prediction and optimisation under uncertainty, contribute to decision-making frameworks, or demonstrate impact in real-world scenarios.

Topics of interest include but are not limited to:

  • Mathematical modelling for AI
  • Statistical learning and inference in AI applications
  • Predictive analytics using Bayesian or frequentist methods
  • Optimisation algorithms for machine learning and data science
  • Uncertainty quantification and decision support
  • Interpretable and transparent AI
  • Hybrid methods combining mathematics, statistics, and data-driven approaches
  • Applications in health, climate, urban systems, finance, manufacturing, transport, and more fields

Prof. Dr. Diwei Zhou
Guest Editor

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. 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

  • predictive modelling
  • machine learning
  • Bayesian inference
  • uncertainty quantification
  • convex and non-convex optimisation
  • graph attention networks
  • graph neural networks
  • intelligent logistics and applications
  • intelligent manufacturing

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Published Papers (1 paper)

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Research

25 pages, 1928 KB  
Article
A Methodological Comparison of Forecasting Models Using KZ Decomposition and Walk-Forward Validation
by Khawla Al-Saeedi, Diwei Zhou, Andrew Fish, Katerina Tsakiri and Antonios Marsellos
Mathematics 2025, 13(21), 3410; https://doi.org/10.3390/math13213410 - 26 Oct 2025
Viewed by 246
Abstract
The accurate forecasting of surface air temperature (T2M) is crucial for climate analysis, agricultural planning, and energy management. This study proposes a novel forecasting framework grounded in structured temporal decomposition. Using the Kolmogorov–Zurbenko (KZ) filter, all predictor variables are decomposed into three physically [...] Read more.
The accurate forecasting of surface air temperature (T2M) is crucial for climate analysis, agricultural planning, and energy management. This study proposes a novel forecasting framework grounded in structured temporal decomposition. Using the Kolmogorov–Zurbenko (KZ) filter, all predictor variables are decomposed into three physically interpretable components: long-term, seasonal, and short-term variations, forming an expanded multi-scale feature space. A central innovation of this framework lies in training a single unified model on the decomposed feature set to predict the original target variable, thereby enabling the direct learning of scale-specific driver–response relationships. We present the first comprehensive benchmarking of this architecture, demonstrating that it consistently enhances the performance of both regularized linear models (Ridge and Lasso) and tree-based ensemble methods (Random Forest and XGBoost). Under rigorous walk-forward validation, the framework substantially outperforms conventional, non-decomposed approaches—for example, XGBoost improves the coefficient of determination (R2) from 0.80 to 0.91. Furthermore, temporal decomposition enhances interpretability by enabling Ridge and Lasso models to achieve performance levels comparable to complex ensembles. Despite these promising results, we acknowledge several limitations: the analysis is restricted to a single geographic location and time span, and short-term components remain challenging to predict due to their stochastic nature and the weaker relevance of predictors. Additionally, the framework’s effectiveness may depend on the optimal selection of KZ parameters and the availability of sufficiently long historical datasets for stable walk-forward validation. Future research could extend this approach to multiple geographic regions, longer time series, adaptive KZ tuning, and specialized short-term modeling strategies. Overall, the proposed framework demonstrates that temporal decomposition of predictors offers a powerful inductive bias, establishing a robust and interpretable paradigm for surface air temperature forecasting. Full article
(This article belongs to the Special Issue Mathematical and Statistical Methods for Prediction and Optimisation in Artificial Intelligence)
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