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Mathematics
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Recent Advances in Time Series Analysis: Methods, Theory, and Applications
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Recent Advances in Time Series Analysis: Methods, Theory, 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: 31 August 2025 | Viewed by 1190

Special Issue Editor

Dr. Mengyu Xu

E-Mail Website
Guest Editor
Department of Statistics and Data Science, University of Central Florida, Orlando, FL 32816, USA
Interests: statistics and data science

Special Issue Information

Dear Colleagues,

Time series analysis plays a key role in understanding the complex dynamics of the data. Demand for such tools arises in a wide range of scientific fields such as finance, engineering, climate, neuroscience, healthcare, etc., which require researchers to infer the trend, uncover hidden patterns, and make informed decisions. Modern applications have imposed contemporary challenges in time analysis, where the data have complex structures and can be high-dimensional, high-volume, non-stationary, non-linear, and non-Gaussian.

This Special Issue will spotlight the recent developments in time series analysis. We cordially invite contributions from the perspectives of methodologies, theories, and applications.

Dr. Mengyu Xu
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

  • time series analysis
  • statistics for time series analysis
  • forecasting
  • trend analysis
  • high-dimensional time series
  • change point
  • application of time series analysis
  • empirical analysis in economic, finance, engineering, healthcare, and neuroscience
  • hypothesis test
  • machine learning methods for time series analysis
  • time-varying network
  • causality
  • spatial time series
  • dynamic models
  • non-linear time series
  • heteroskedasticity
  • prediction
  • resampling
  • ARMA models
  • VAR models
  • factor models

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Published Papers (1 paper)

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Research

15 pages, 3519 KiB  
Article
Effectiveness of PEER Intervention on Older Adults’ Physical Activity Time Series Using Smoothing Spline ANOVA
by Yi Liu, Chang Liu, Liqiang Ni, Wei Zhang, Chen Chen, Janet Lopez, Hao Zheng, Ladda Thiamwong and Rui Xie
Mathematics 2025, 13(3), 516; https://doi.org/10.3390/math13030516 - 4 Feb 2025
Viewed by 883
Abstract
Falls are a major cause of injury among older adults. The Physio-fEedback Exercise pRogram (PEER) combines physio-feedback, cognitive reframing, and guided exercises to reduce fall risk. However, its impact on physical activity (PA) over time is underexplored. Functional time-series analysis offers insight into [...] Read more.
Falls are a major cause of injury among older adults. The Physio-fEedback Exercise pRogram (PEER) combines physio-feedback, cognitive reframing, and guided exercises to reduce fall risk. However, its impact on physical activity (PA) over time is underexplored. Functional time-series analysis offers insight into behavior patterns and sustainability. This preliminary study assessed PEER’s effectiveness in improving PA levels immediately and over time. A total of 64 community-dwelling older adults were cluster-randomized into PEER (N=33) or control groups (N=31). Participants wore Fitbit trackers, generating time-series data on activity. The PEER group completed an 8-week program, while the control group received CDC fall prevention pamphlets. PA data were analyzed using smoothing spline analysis of variance (SSANOVA), chosen for its flexibility in modeling complex, non-linear relationships in time-series data and its ability to handle skewed distributions and repeated measures. Unlike traditional parametric models, SSANOVA decomposes temporal trends into interpretable components, capturing both smooth trends and abrupt changes, such as those occurring on group workout days. This capability ensures robust and nuanced analysis of intervention effects. Results showed PEER participants significantly increased evenly and had very active minutes and reduced sedentary behavior during the intervention. No significant effect was found for light active minutes. Specifically, during the intervention period, PEER participants engaged in an average of 6.7% fewer sedentary minutes per day, 13.8% additional fairly active minutes per day, and 2.8% additional very active minutes per day compared to the control group. While the reduction in sedentary minutes and increase in fairly active minutes were not statistically significant, the increase in very active minutes was significant. However, our functional time-series analysis revealed these improvements diminished over the 15-week follow-up, indicating challenges in maintaining PA. In conclusion, PEER boosts PA and reduces sedentary behavior short-term, but strategies are needed to sustain these benefits. In conclusion, PEER boosts PA and reduces sedentary behavior short-term, but strategies are needed to sustain these benefits. Public health policies should emphasize technology-driven fall risk assessments, community-based prevention programs, and initiatives that promote physical activity, home safety, and chronic condition management. Full article
(This article belongs to the Special Issue Recent Advances in Time Series Analysis: Methods, Theory, and Applications)
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