Advances in Time Series Analysis and Forecasting

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2836

Special Issue Editors


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Guest Editor
Department of Statistics, University of Campinas, Campinas 13083-859, Brazil
Interests: time series analysis; financial econometrics; time series econometrics

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Guest Editor
Sao Paulo School of Economics, Getulio Vargas Foundation (FGV), CEQEF, Sao Paulo 01332-000, Brazil
Interests: forecast; time series econometrics; empirical finance; financial econometrics

Special Issue Information

Dear Colleagues,

We are welcoming submissions for a Special Issue entitled “Advances in Time Series Analysis and Forecasting”. Collecting data over time occurs naturally in almost all fields of studies and it materializes in many ways. It can be collected continuously or discretely, equally or unequally spaced, with short or long time series, as univariate or high dimensional, with missing values, or with structural breaks, etc. Therefore, it is natural that the field encompasses many models and methods that are continuously evolving. The aim of this Special Issue is to present some of the most recent advances in the field. Theoretical papers and novel applications are welcome, with studies addressing the analysis of complex data, computational issues, forecasting, high dimensional studies, change of regime, outliers and robust methods, but submissions are not restricted to these topics. Review papers are also welcome.

Prof. Dr. Luiz Koodi Hotta
Prof. Dr. Pedro Luiz Valls Pereira
Guest Editors

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Keywords

  • econometrical and technical applications
  • forecasting and assessment of forecasts
  • multiple time series and cointegration
  • time series with changes in regime
  • stationarity of time series and long-term memory
  • state–space models and Kalman filters
  • filtering of stochastic processes
  • forecasting
  • risk analysis
  • high dimensional
  • outliers and robust methods
  • complex data
  • computational methods

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Published Papers (2 papers)

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Research

23 pages, 401 KiB  
Article
Combining Generalized Linear Autoregressive Moving Average and Bootstrap Models for Analyzing Time Series of Respiratory Diseases and Air Pollutants
by Ana Julia Alves Camara, Valdério Anselmo Reisen, Glaura Conceicao Franco and Pascal Bondon
Mathematics 2025, 13(5), 859; https://doi.org/10.3390/math13050859 - 5 Mar 2025
Viewed by 489
Abstract
The generalized linear autoregressive moving-average model (GLARMA) has been used in epidemiology to evaluate the impact of pollutants on health. These effects are quantified through the relative risk (RR) measure, which inference can be based on the asymptotic properties of the maximum likelihood [...] Read more.
The generalized linear autoregressive moving-average model (GLARMA) has been used in epidemiology to evaluate the impact of pollutants on health. These effects are quantified through the relative risk (RR) measure, which inference can be based on the asymptotic properties of the maximum likelihood estimator. However, for small series, this can be troublesome. This work studies different types of bootstrap confidence intervals (CIs) for the RR. The simulation study revealed that the model parameter related to the data’s autocorrelation could influence the intervals’ coverage. Problems could arise when covariates present an autocorrelation structure. To solve this, using the vector autoregressive (VAR) filter in the covariates is suggested. Full article
(This article belongs to the Special Issue Advances in Time Series Analysis and Forecasting)
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20 pages, 444 KiB  
Article
Time Series Forecasting with Many Predictors
by Shuo-Chieh Huang and Ruey S. Tsay
Mathematics 2024, 12(15), 2336; https://doi.org/10.3390/math12152336 - 26 Jul 2024
Cited by 1 | Viewed by 1316
Abstract
We propose a novel approach for time series forecasting with many predictors, referred to as the GO-sdPCA, in this paper. The approach employs a variable selection method known as the group orthogonal greedy algorithm and the high-dimensional Akaike information criterion to mitigate the [...] Read more.
We propose a novel approach for time series forecasting with many predictors, referred to as the GO-sdPCA, in this paper. The approach employs a variable selection method known as the group orthogonal greedy algorithm and the high-dimensional Akaike information criterion to mitigate the impact of irrelevant predictors. Moreover, a novel technique, called peeling, is used to boost the variable selection procedure so that many factor-relevant predictors can be included in prediction. Finally, the supervised dynamic principal component analysis (sdPCA) method is adopted to account for the dynamic information in factor recovery. In simulation studies, we found that the proposed method adapts well to unknown degrees of sparsity and factor strength, which results in good performance, even when the number of relevant predictors is large compared to the sample size. Applying to economic and environmental studies, the proposed method consistently performs well compared to some commonly used benchmarks in one-step-ahead out-sample forecasts. Full article
(This article belongs to the Special Issue Advances in Time Series Analysis and Forecasting)
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