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Mathematics
Special Issues
Time Series Analysis and Data Analytics: Methods and Applications
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Time Series Analysis and Data Analytics: Methods and Applications

A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 685

Editor

Dr. Anli Ji

E-Mail Website
Guest Editor
Department of Computer Science, California State University, Fullerton, CA, USA
Interests: machine learning; time series mining; big data analysis

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advances in the methods and applications used for time series analysis and data analytics, addressing the growing need to extract meaningful insights from complex, high-dimensional, and large-scale datasets. With the rapid development of data collection technologies across domains such as finance, environmental systems, healthcare, and smart infrastructure, there is increasing demand for robust mathematical frameworks and scalable computational approaches.

This Issue aims to collate contributions that develop novel methodologies in time series modeling, including statistical learning, deep learning architectures, and hybrid approaches. Emphasis is placed on handling challenges such as non-stationarity, high noise levels, missing data, and real-time data streams. In addition, submissions exploring theoretical foundations, algorithmic efficiency, and model interpretability are highly encouraged.

This Special Issue also welcomes interdisciplinary applications where time series and data analytics are used to support decision-making and predictive modeling in real-world systems. Contributions demonstrating practical implementation, validation on large datasets, and integration with emerging technologies will be particularly valuable.

Dr. Anli Ji
Guest Editor

Manuscript Submission Information

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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-anonymized 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
  • data analytics
  • machine learning
  • data-driven methods

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Published Papers (1 paper)

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Research

34 pages, 2132 KB  
Article
Finite-Sample Conformal Risk Bounds for Joint Value-at-Risk and Expected-Shortfall Forecasting Under Non-Exchangeable Financial Time Series
by Yuxin Ye, Xuhua Qiu, Kunjie Zhu and Miltos Ladikas
Mathematics 2026, 14(15), 2847; https://doi.org/10.3390/math14152847 - 6 Aug 2026
Viewed by 451
Abstract
Financial tail-risk observations are non-exchangeable: serial dependence and regime shifts make their joint law depend on the time ordering, invalidating the exchangeability that standard conformal guarantees assume, and expected shortfall is not elicitable on its own, so a forecaster cannot be calibrated to [...] Read more.
Financial tail-risk observations are non-exchangeable: serial dependence and regime shifts make their joint law depend on the time ordering, invalidating the exchangeability that standard conformal guarantees assume, and expected shortfall is not elicitable on its own, so a forecaster cannot be calibrated to it as a quantile is to its coverage. We ask whether a black-box value-at-risk and expected-shortfall forecaster can be calibrated under such dependence while retaining finite-sample guarantees. We tune a single inflation parameter by conformal risk control on a bounded monotone loss that couples value-at-risk breach frequency with breach magnitude normalised by the model’s predicted value-at-risk–expected-shortfall gap; the guarantee is thus for a tail-gap-normalised exceedance-severity surrogate, and its expected-shortfall reading depends on the predicted gap being a sound tail-gap estimate. Under exchangeability, the method gives finite-sample expected-risk control; for dependent data we invoke a non-exchangeable swap-distance bound and add, for separated calibration points, a regime-drift bound with an explicit cumulative β-mixing cost, plus a high-probability realised-path statement and a heavy-tail rate of order D(p−1)/p. Building regimes causally from previous-month FRED-MD vintages across eight exchange rates, a Bitcoin series, and the GIFT-Eval finance domain, the weighted controller attains a 2.51% violation rate and a Fissler–Ziegel score of 0.431 against 0.441 and 0.439 for the strongest conformal baselines—an incremental gain, not significant at the 5% level, that concentrates in turbulent regimes and at matched capital, supporting calibration of a joint frequency-and-normalised-severity budget rather than distribution-free control of the expected-shortfall forecast itself. Full article
(This article belongs to the Special Issue Time Series Analysis and Data Analytics: Methods and Applications)
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