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Axioms
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Time Series: Theory and Applications
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Time Series: Theory and Applications

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (10 June 2023) | Viewed by 5742

Special Issue Editor

Dr. Paolo Pagnottoni

E-Mail Website
Guest Editor
Department of Economics and Management, University of Pavia, Pavia, Italy
Interests: statistics; time series analysis; financial econometrics; complex systems; cryptocurrencies

Special Issue Information

Dear Colleagues,

The study of time series is dealt with a wide variety of subjects, including statistics, mathematics, physics, and econometrics; both from a theoretical and an applied viewpoint. The domains of application of time series analysis are a large number as well, spanning from social sciences, economics, finance to medicine, biology, climate, information science, and many others.

This Special Issue aims to bring together contributions concerning time series analyses from a wide variety of study fields and applicative domains. This Special Issue seeks studies on novel methodologies or applications in both univariate and multivariate time series and stochastic processes. In particular, the Special Issue welcomes submissions concerning time series from the following areas, or in between the areas of (non-exhaustive list):

  • Statistics
  • Mathematics
  • Physics
  • Econometrics

The Special Issue’s areas of interest of applications include but are not limited to the following wide range of topics:

  • Economics
  • Finance
  • Social Sciences
  • Meteorology and Climate Science
  • Biology
  • Engineering

The Special Issue is broad, and theoretical and empirical contributions concerning any of the mentioned issues are more than welcome. Likewise, we welcome submissions of papers with socially relevant research questions. Papers in between the fields and applicative domains elicited before are more than welcome as well, and the Special Issue is open to receiving further ideas not included in the listed topics.

Dr. Paolo Pagnottoni
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. Axioms is an international peer-reviewed open access monthly 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 2400 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

  • univariate time series
  • multivariate time series
  • stochastic processes
  • statistical physics

Published Papers (3 papers)

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Research

29 pages, 4307 KiB  
Article
Laplacian Split-BREAK Process with Application in Dynamic Analysis of the World Oil and Gas Market
by Vladica S. Stojanović, Hassan S. Bakouch, Eugen Ljajko and Ivan Božović
Axioms 2023, 12(7), 622; https://doi.org/10.3390/axioms12070622 - 21 Jun 2023
Cited by 1 | Viewed by 1092
Abstract
This manuscript deals with a novel, nonlinear, and non-stationary stochastic model with symmetric, Laplacian distributed innovations. The obtained model, named Laplacian Split-BREAK (LSB) process, is intended for dynamic analysis of time series with pronounced and permanent fluctuations. By using the method of characteristic [...] Read more.
This manuscript deals with a novel, nonlinear, and non-stationary stochastic model with symmetric, Laplacian distributed innovations. The obtained model, named Laplacian Split-BREAK (LSB) process, is intended for dynamic analysis of time series with pronounced and permanent fluctuations. By using the method of characteristic functions (CFs), the basic stochastic properties of the LSB process are proven, with a special emphasis on its asymptotic behaviour. The different procedures for estimating its parameters are also given, along with numerical simulations of the obtained estimators. Finally, it has been shown that the LSB process, as an adequate stochastic model, can be applied in the analysis of dynamics in the world market of crude oil and natural gas. Full article
(This article belongs to the Special Issue Time Series: Theory and Applications)
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20 pages, 980 KiB  
Article
Parameters Estimation in Non-Negative Integer-Valued Time Series: Approach Based on Probability Generating Functions
by Vladica Stojanović, Eugen Ljajko and Marina Tošić
Axioms 2023, 12(2), 112; https://doi.org/10.3390/axioms12020112 - 21 Jan 2023
Cited by 2 | Viewed by 1431
Abstract
This manuscript deals with a parameter estimation of a non-negative integer-valued (NNIV) time series based on the so-called probability generating function (PGF) method. The theoretical background of the PGF estimation technique for a very general, stationary class of NNIV time series is described, [...] Read more.
This manuscript deals with a parameter estimation of a non-negative integer-valued (NNIV) time series based on the so-called probability generating function (PGF) method. The theoretical background of the PGF estimation technique for a very general, stationary class of NNIV time series is described, as well as the asymptotic properties of the obtained estimates. After that, a particular emphasis is given to PGF estimators of independent identical distributed (IID) and integer-valued non-negative autoregressive (INAR) series. A Monte Carlo study of the thus obtained PGF estimates, based on a numerical integration of the appropriate objective function, is also presented. For this purpose, numerical quadrature formulas were computed using Gegenbauer orthogonal polynomials. Finally, the application of the PGF estimators in the dynamic analysis of some actual data is given. Full article
(This article belongs to the Special Issue Time Series: Theory and Applications)
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18 pages, 6805 KiB  
Article
Investigation and Analysis of Sea Surface Temperature and Precipitation of the Southern Caspian Sea Using Wavelet Analysis
by Mahboubeh Molavi-Arabshahi, Jafar Azizpour, Omid Nikan, Abdolmajid Naderi Beni and António M. Lopes
Axioms 2023, 12(1), 10; https://doi.org/10.3390/axioms12010010 - 22 Dec 2022
Viewed by 1227
Abstract
Most time series from real-world processes are stained with noise. Therefore, much attention should be paid to data noise removal techniques. In this study, we use the family of biorthogonal wavelet, high-pass, and low-pass filters, to investigate the power of the wavelet method [...] Read more.
Most time series from real-world processes are stained with noise. Therefore, much attention should be paid to data noise removal techniques. In this study, we use the family of biorthogonal wavelet, high-pass, and low-pass filters, to investigate the power of the wavelet method in removing noise from time series data. Using the wavelet discrete transformation, the variability of precipitation and sea surface temperature is analyzed for a southern region of the Caspian Sea. At each stage of decomposition, the previous wave is decomposed into two waves. In this research, the SST and precipitation data are decomposed into several levels based on discrete wavelet transformation. In each level of decomposition, the previous wave is decomposed into two waves. This can be done many times and at each stage, reducing the amount of data. This method is reversible, and the original wave can be reconstructed using the decomposed values. In the study of discrete wavelet transforms, it was observed that the analysis based on wavelets leads to more accurate results. The reconstruction error in the proposed method is shown to be very small. Full article
(This article belongs to the Special Issue Time Series: Theory and Applications)
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