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Mathematics
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Statistical Forecasting: Theories, Methods and Applications
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Statistical Forecasting: Theories, Methods and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 5466

Special Issue Editor

Prof. Dr. S. Ejaz Ahmed

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, Brock University, St. Catharines, ON L2S 3A1, Canada
Interests: model selection; post-estimation and prediction; shrinkage and empirical Bayes; Bayesian data analysis; machine learning; business; information science; statistical genetics; image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Statistical forecasting is widely applicable in various fields, such as economics, finance, meteorology, environmental sciences, biology, and healthcare, and it plays a crucial role in decision making and planning by individuals, organizations, and industries. It helps in identifying patterns, trends, and relationships in historical data to make informed predictions about future events or outcomes.

This Special Issue "Statistical Forecasting: Theories, Methods and Applications" in Mathematics aims to explore this important field and the use of statistical techniques to predict future values or trends. The focus is on the role and effect of various statistical methods in data prediction.

We welcome researchers to explore the latest advancements and challenges in statistical forecasting and to promote the exchange of ideas and knowledge in this field via research on the theoretical aspects of statistical forecasting. The topics of interest include, but are not limited to, time series analysis, regression analysis, machine learning techniques, Bayesian data analysis, multivariate analysis, and statistical diagnostics. Additionally, we place equal emphasis on applied aspects of statistical forecasting, and the role and effect of various statistical methods in data prediction; the fields of application include, but are not limited to, economics, finance, and environment and health sciences. 

Prof. Dr. S. Ejaz Ahmed
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

  • statistical forecasting
  • predictive modeling
  • Bayesian statistics
  • distribution theory and its applications
  • risk forecasting
  • financial statistics

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Published Papers (4 papers)

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Research

12 pages, 280 KiB  
Article
Optimizing Accuracy, Recall, Specificity, and Precision Using ILP
by Arash Marioriyad and Pouria Ramazi
Mathematics 2025, 13(7), 1059; https://doi.org/10.3390/math13071059 - 25 Mar 2025
Viewed by 321
Abstract
Accuracy, recall, specificity, and precision are key performance measures for binary classifiers. To obtain these measures, the probabilities generated by classifiers must be converted into deterministic labels using a threshold. Exhaustive search methods can be computationally expensive, prompting the need for a more [...] Read more.
Accuracy, recall, specificity, and precision are key performance measures for binary classifiers. To obtain these measures, the probabilities generated by classifiers must be converted into deterministic labels using a threshold. Exhaustive search methods can be computationally expensive, prompting the need for a more efficient solution. We propose an integer linear programming (ILP) formulation to find the threshold that maximizes any linear combination of these measures. Simulations and experiments on four real-world datasets demonstrate that our approach identifies the optimal threshold orders of magnitude faster than an exhaustive search. This work establishes ILP as an efficient tool for optimizing classifier performance. Full article
(This article belongs to the Special Issue Statistical Forecasting: Theories, Methods and Applications)
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19 pages, 2214 KiB  
Article
Optimal Weighted Markov Model and Markov Optimal Weighted Combination Model with Their Application in Hunan’s Gross Domestic Product
by Dewang Li, Chingfei Luo and Meilan Qiu
Mathematics 2025, 13(3), 533; https://doi.org/10.3390/math13030533 - 5 Feb 2025
Viewed by 481
Abstract
In this paper, we mainly establish an optimal weighted Markov model to predict the GDP of Hunan Province from 2017 to 2023. The new model is composed of a fractional grey model and a quadratic function regression model weighted combination and is obtained [...] Read more.
In this paper, we mainly establish an optimal weighted Markov model to predict the GDP of Hunan Province from 2017 to 2023. The new model is composed of a fractional grey model and a quadratic function regression model weighted combination and is obtained through Markov correction. First, the optimal order r of the fractional grey model (FGM) is determined by using the particle swarm optimization (PSO) algorithm, and the FGM model is established. Second, a quadratic regression model is established based on the scatter plot of the data. Then, the optimal weighted Markov model (OWMKM) is obtained by combining the above two sub-models (i.e., the optimal weighted combination model (OWM)) and using Markov correction. Finally, the new model is applied to estimate and predict the GDP of Hunan Province from 2017 to 2023. The forecast results show that the four statistical measures of the optimal weighted Markov model, such as MAPE, RMSE, R2, and STD, are superior to the optimal weighted combination model (OWM), the nonlinear auto regressive model (NAR) and the autoregressive integrated moving average model (ARIMA), which indicates that our new model has strong fitting and higher accuracy. We establish the quadratic regression Markov model (QFRMKM), the fractional grey Markov model (FGMKM), and the optimal combination model of these two sub-models (MKMOWM). The effects of the MKMOWM and OWMKM are compared. This research provides a scientifically reliable reference and has significant importance for understanding the development trends of the economy in Hunan Province, enabling governments and companies to make sound and reliable decisions and plans. Full article
(This article belongs to the Special Issue Statistical Forecasting: Theories, Methods and Applications)
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21 pages, 1227 KiB  
Article
netQDA: Local Network-Guided High-Dimensional Quadratic Discriminant Analysis
by Xueping Zhou, Wei Chen and Yanming Li
Mathematics 2024, 12(23), 3823; https://doi.org/10.3390/math12233823 - 3 Dec 2024
Viewed by 722
Abstract
Quadratic Discriminant Analysis (QDA) is a well-known and flexible classification method that considers differences between groups based on both mean and covariance structures. However, the connection structures of high-dimensional predictors are usually not explicitly incorporated into modeling. In this work, we propose a [...] Read more.
Quadratic Discriminant Analysis (QDA) is a well-known and flexible classification method that considers differences between groups based on both mean and covariance structures. However, the connection structures of high-dimensional predictors are usually not explicitly incorporated into modeling. In this work, we propose a local network-guided QDA method that integrates the local connection structures of high-dimensional predictors. In the context of gene expression research, our method can identify genes that show differential expression levels as well as gene networks that exhibit different connection patterns between various biological state groups, thereby enhancing our understanding of underlying biological mechanisms. Extensive simulations and real data applications demonstrate its superior performance in both feature selection and outcome classification compared to commonly used discriminant analysis methods. Full article
(This article belongs to the Special Issue Statistical Forecasting: Theories, Methods and Applications)
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35 pages, 4835 KiB  
Article
Nonparametric Copula Density Estimation Methodologies
by Serge B. Provost and Yishan Zang
Mathematics 2024, 12(3), 398; https://doi.org/10.3390/math12030398 - 26 Jan 2024
Cited by 2 | Viewed by 2492
Abstract
This paper proposes several methodologies whose objective consists of securing copula density estimates. More specifically, this aim will be achieved by differentiating bivariate least-squares polynomials fitted to Deheuvels’ empirical copulas, by making use of Bernstein’s approximating polynomials of appropriately selected orders; by differentiating [...] Read more.
This paper proposes several methodologies whose objective consists of securing copula density estimates. More specifically, this aim will be achieved by differentiating bivariate least-squares polynomials fitted to Deheuvels’ empirical copulas, by making use of Bernstein’s approximating polynomials of appropriately selected orders; by differentiating linearized distribution functions evaluated at optimally spaced grid points; and by implementing the kernel density estimation technique in conjunction with a repositioning of the pseudo-observations and a certain criterion for determining suitable bandwidths. Smoother representations of such density estimates can further be secured by approximating them by means of moment-based bivariate polynomials. The various copula density estimation techniques being advocated herein are successfully applied to an actual dataset as well as a random sample generated from a known distribution. Full article
(This article belongs to the Special Issue Statistical Forecasting: Theories, Methods and Applications)
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