Time Series Analysis: Methods and Applications

Dear Colleagues,

Time series analysis has become increasingly vital in today's data-driven world, with applications spanning finance, economics, healthcare, public health surveillance, industrial systems, and engineering. The rapid expansion of large-scale, high-frequency, and heterogeneous datasets has introduced new challenges, including non-stationarity, high dimensionality, missing or contaminated observations, and structural changes in dynamic systems. Addressing these challenges requires methodological innovation and the integration of advanced statistical, computational, and machine learning techniques.

This Special Issue seeks to bring together cutting-edge research on time series analysis, covering both theoretical developments and applied solutions. Topics of interest include nonlinear and robust methods, multivariate and functional time series, semiparametric and Bayesian approaches, stochastic process modeling, forecasting and prediction under uncertainty, and advanced regression methodologies. In addition, we welcome contributions that address real-world challenges, such as modeling epidemics (SEIRD/SEIRDV models), risk management in economics and finance, longitudinal and survival data analysis, industrial quality control, and interdisciplinary applications in biomedical, educational, and social sciences.

This Special Issue aims to advance the field of time series analysis by publishing high-quality research that bridges theory and practice, fostering innovation and expanding the scope of applications across diverse domains. We invite submissions of original research articles, methodological papers, reviews, and short communications.

Dr. Abdel Salam G. Abdel-Salam
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.

• time series analysis
• nonlinear and non-stationary processes
• multivariate time series
• functional time series
• robust and semiparametric methods
• stochastic process modeling
• Bayesian inference and Bayesian control charts
• machine learning and deep learning for time series
• forecasting and prediction
• epidemiological and SEIRD/SEIRDV models
• public health surveillance
• financial and economic time series
• industrial statistics and quality control
• profile monitoring
• high-dimensional and spatio-temporal data
• missing and contaminated data
• longitudinal and survival data
• operations research and optimization
• risk modeling and economic capital models