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Modern Time Series Analysis
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Modern Time Series Analysis

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 18286

Special Issue Editors

Dr. Magda Sofia Valério Monteiro

E-Mail Website
Guest Editor
Águeda School of Technology and Management (ESTGA) & Center for Research and Development in Mathematics and Applications (CIDMA), University of Aveiro, 3754-909 Águeda, Portugal
Interests: time series analysis; statistical modeling; state-space models; statistical inference; data analysis; count data; environmental statistics
Special Issues, Collections and Topics in MDPI journals
Dr. Marco André da Silva Costa

E-Mail Website
Guest Editor
Águeda School of Technology and Management (ESTGA) & Center for Research and Development in Mathematics and Applications (CIDMA), University of Aveiro, 3754-909 Águeda, Portugal
Interests: state-space models; Kalman filtering; linear models; time series analysis; environmental statistics; data analysis; distribution-free estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce a Special Issue on “Modern Time Series Analysis”.

This Special Issue will focus on recent advances in the field of time series analysis and forecasting.

This issue welcomes original contributions concerning the modern time series analysis including modeling and forecasting, advances in high-dimensional multivariate modeling, advances in online learning time series, big data analysis, and also forecasting in real problems.

Special Issue topics include (but are not limited to) the following:

  • Distribution-free methods for time series
  • Outliers in time series data
  • Non-linear models for time series
  • Models for extremes in time series
  • High-dimensional multivariate modeling
  • Models for count time series
  • Econometric models
  • Artificial neural networks and machine learning
  • Time series analysis with computational intelligence
  • Change-point detection for time series
  • Multivariate time series models
  • Forecasting from complex/big data
  • Forecasting in real problems

Dr. Magda Sofia Valério Monteiro
Dr. Marco André da Silva Costa
Guest Editors

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. Stats is an international peer-reviewed open access quarterly 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 1600 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
  • distribution-free estimation
  • outliers in time series
  • count data
  • state-space models
  • big data time series
  • extreme time series
  • forecasting

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Published Papers (6 papers)

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Research

14 pages, 1112 KiB  
Article
A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors
by Juan D. Borrero, Jesús Mariscal and Alfonso Vargas-Sánchez
Stats 2022, 5(4), 1145-1158; https://doi.org/10.3390/stats5040068 - 16 Nov 2022
Cited by 4 | Viewed by 3146
Abstract
Accurate time series prediction techniques are becoming fundamental to modern decision support systems. As massive data processing develops in its practicality, machine learning (ML) techniques applied to time series can automate and improve prediction models. The radical novelty of this paper is the [...] Read more.
Accurate time series prediction techniques are becoming fundamental to modern decision support systems. As massive data processing develops in its practicality, machine learning (ML) techniques applied to time series can automate and improve prediction models. The radical novelty of this paper is the development of a hybrid model that combines a new approach to the classical Kalman filter with machine learning techniques, i.e., support vector regression (SVR) and nonlinear autoregressive (NAR) neural networks, to improve the performance of existing predictive models. The proposed hybrid model uses, on the one hand, an improved Kalman filter method that eliminates the convergence problems of time series data with large error variance and, on the other hand, an ML algorithm as a correction factor to predict the model error. The results reveal that our hybrid models obtain accurate predictions, substantially reducing the root mean square and absolute mean errors compared to the classical and alternative Kalman filter models and achieving a goodness of fit greater than 0.95. Furthermore, the generalization of this algorithm was confirmed by its validation in two different scenarios. Full article
(This article belongs to the Special Issue Modern Time Series Analysis)
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25 pages, 4695 KiB  
Article
Modeling Realized Variance with Realized Quarticity
by Hiroyuki Kawakatsu
Stats 2022, 5(3), 856-880; https://doi.org/10.3390/stats5030050 - 7 Sep 2022
Viewed by 2618
Abstract
This paper proposes a model for realized variance that exploits information in realized quarticity. The realized variance and quarticity measures are both highly persistent and highly correlated with each other. The proposed model incorporates information from the observed realized quarticity process via autoregressive [...] Read more.
This paper proposes a model for realized variance that exploits information in realized quarticity. The realized variance and quarticity measures are both highly persistent and highly correlated with each other. The proposed model incorporates information from the observed realized quarticity process via autoregressive conditional variance dynamics. It exploits conditional dependence in higher order (fourth) moments in analogy to the class of GARCH models exploit conditional dependence in second moments. Full article
(This article belongs to the Special Issue Modern Time Series Analysis)
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13 pages, 456 KiB  
Article
Bootstrap Prediction Intervals of Temporal Disaggregation
by Bu Hyoung Lee
Stats 2022, 5(1), 190-202; https://doi.org/10.3390/stats5010013 - 18 Feb 2022
Cited by 5 | Viewed by 2470
Abstract
In this article, we propose an interval estimation method to trace an unknown disaggregate series within certain bandwidths. First, we consider two model-based disaggregation methods called the GLS disaggregation and the ARIMA disaggregation. Then, we develop iterative steps to construct AR-sieve bootstrap prediction [...] Read more.
In this article, we propose an interval estimation method to trace an unknown disaggregate series within certain bandwidths. First, we consider two model-based disaggregation methods called the GLS disaggregation and the ARIMA disaggregation. Then, we develop iterative steps to construct AR-sieve bootstrap prediction intervals for model-based temporal disaggregation. As an illustration, we analyze the quarterly total balances of U.S. international trade in goods and services between the first quarter of 1992 and the fourth quarter of 2020. Full article
(This article belongs to the Special Issue Modern Time Series Analysis)
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17 pages, 591 KiB  
Article
A General Description of Growth Trends
by Moshe Elitzur
Stats 2022, 5(1), 111-127; https://doi.org/10.3390/stats5010008 - 3 Feb 2022
Viewed by 2076
Abstract
Time series that display periodicity can be described with a Fourier expansion. In a similar vein, a recently developed formalism enables the description of growth patterns with the optimal number of parameters. The method has been applied to the growth of national GDP, [...] Read more.
Time series that display periodicity can be described with a Fourier expansion. In a similar vein, a recently developed formalism enables the description of growth patterns with the optimal number of parameters. The method has been applied to the growth of national GDP, population and the COVID-19 pandemic; in all cases, the deviations of long-term growth patterns from purely exponential required no more than two additional parameters, mostly only one. Here, I utilize the new framework to develop a unified formulation for all functions that describe growth deceleration, wherein the growth rate decreases with time. The result offers the prospects for a new general tool for trend removal in time-series analysis. Full article
(This article belongs to the Special Issue Modern Time Series Analysis)
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19 pages, 1361 KiB  
Article
A Noncentral Lindley Construction Illustrated in an INAR(1) Environment
by Johannes Ferreira and Ané van der Merwe
Stats 2022, 5(1), 70-88; https://doi.org/10.3390/stats5010005 - 10 Jan 2022
Cited by 2 | Viewed by 5105
Abstract
This paper proposes a previously unconsidered generalization of the Lindley distribution by allowing for a measure of noncentrality. Essential structural characteristics are investigated and derived in explicit and tractable forms, and the estimability of the model is illustrated via the fit of this [...] Read more.
This paper proposes a previously unconsidered generalization of the Lindley distribution by allowing for a measure of noncentrality. Essential structural characteristics are investigated and derived in explicit and tractable forms, and the estimability of the model is illustrated via the fit of this developed model to real data. Subsequently, this model is used as a candidate for the parameter of a Poisson model, which allows for departure from the usual equidispersion restriction that the Poisson offers when modelling count data. This Poisson-noncentral Lindley is also systematically investigated and characteristics are derived. The value of this count model is illustrated and implemented as the count error distribution in an integer autoregressive environment, and juxtaposed against other popular models. The effect of the systematically-induced noncentrality parameter is illustrated and paves the way for future flexible modelling not only as a standalone contender in continuous Lindley-type scenarios but also in discrete and discrete time series scenarios when the often-encountered equidispersed assumption is not adhered to in practical data environments. Full article
(This article belongs to the Special Issue Modern Time Series Analysis)
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26 pages, 840 KiB  
Article
Optimal Neighborhood Selection for AR-ARCH Random Fields with Application to Mortality
by Paul Doukhan, Joseph Rynkiewicz and Yahia Salhi
Stats 2022, 5(1), 26-51; https://doi.org/10.3390/stats5010003 - 30 Dec 2021
Viewed by 2192
Abstract
This article proposes an optimal and robust methodology for model selection. The model of interest is a parsimonious alternative framework for modeling the stochastic dynamics of mortality improvement rates introduced recently in the literature. The approach models mortality improvements using a random field [...] Read more.
This article proposes an optimal and robust methodology for model selection. The model of interest is a parsimonious alternative framework for modeling the stochastic dynamics of mortality improvement rates introduced recently in the literature. The approach models mortality improvements using a random field specification with a given causal structure instead of the commonly used factor-based decomposition framework. It captures some well-documented stylized facts of mortality behavior including: dependencies among adjacent cohorts, the cohort effects, cross-generation correlations, and the conditional heteroskedasticity of mortality. Such a class of models is a generalization of the now widely used AR-ARCH models for univariate processes. A the framework is general, it was investigated and illustrated a simple variant called the three-level memory model. However, it is not clear which is the best parameterization to use for specific mortality uses. In this paper, we investigate the optimal model choice and parameter selection among potential and candidate models. More formally, we propose a methodology well-suited to such a random field able to select thebest model in the sense that the model is not only correct but also most economical among all thecorrectmodels. Formally, we show that a criterion based on a penalization of the log-likelihood, e.g., the using of the Bayesian Information Criterion, is consistent. Finally, we investigate the methodology based on Monte-Carlo experiments as well as real-world datasets. Full article
(This article belongs to the Special Issue Modern Time Series Analysis)
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