Statistical Modelling and Time Series Analysis: Theory and Multidisciplinary Application

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue focused on Statistical Modelling and Time Series Analysis, with an emphasis on both theoretical innovation and multidisciplinary applications. This issue aims to serve as a platform for the exchange of novel ideas, methodologies, and applied research that address the complexities of modeling time-dependent phenomena.

In recent decades, time series analysis and statistical modelling have become increasingly vital across scientific disciplines. With the explosive growth of data collection in areas such as climate science, epidemiology, finance, engineering, and social behavior, the need for robust, interpretable, and scalable methods has never been greater.

Traditional models, while foundational, are often challenged by modern datasets that are high-dimensional, non-linear, irregularly sampled, or exhibit structural breaks and dependencies over time. At the same time, advances in computational power and machine learning have opened new avenues for dynamic modelling, real-time forecasting, and data fusion from heterogeneous sources.

This Special Issue seeks to spotlight these developments, highlighting both the theoretical underpinnings and practical impact of state-of-the-art methods in time series analysis and statistical modelling.

We welcome original research articles, comprehensive reviews, and case studies that address the following:

• Development of new statistical models for time series data;
• Estimation and inference techniques for dynamic models;
• Bayesian, frequentist, and hybrid methodologies;
• Time series forecasting, signal extraction, and trend analysis;
• Multivariate and spatial–temporal modelling approaches;
• Machine learning and AI-driven methods applied to time series;
• Applications in economics, climate science, epidemiology, health, engineering, and more;
• Software tools, computational strategies, and reproducible research.

This Special Issue is designed to bridge the gap between theory and practice by encouraging contributions that not only advance statistical methodologies but also demonstrate their relevance through real-world applications. Our goal is to foster interdisciplinary collaboration and to underline the pivotal role of time series analysis and statistical modelling in solving contemporary scientific and societal challenges.

All submissions will be peer reviewed to ensure high standards of academic rigor and innovation.

We look forward to receiving your contributions.

Dr. Abolfazl Saghafi
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.

• machine learning
• statistical modeling
• time series analysis
• time series classification
• forecasting
• risk analysis
• time-varying parameters
• temporal data mining
• signal processing
• computational analysis