Analysis of Autocorrelation Function of Random Processes by Higher Degree F-transform

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F21%3AA22025DQ" target="_blank" >RIV/61988987:17610/21:A22025DQ - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s00500-020-05543-x" target="_blank" >https://link.springer.com/article/10.1007/s00500-020-05543-x</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Analysis of Autocorrelation Function of Random Processes by Higher Degree F-transform

Original language description

The autocorrelation function of a random process is one of the essential tools in the description of variability that is successfully applied in many scientific fields such as statistical signal processing or financial time series analysis and forecasting. The aim of the paper is to provide the analysis of the autocorrelation function of a stochastic process with the help of the fuzzy transform of higher degree, where a bivariate fuzzy transform is newly introduced in the tensor product of polynomial spaces. We prove several approximation properties of the tensor product based fuzzy transform and show that such a bivariate fuzzy transform of multiplicative separable functions can be easily obtained as a product of univariate fuzzy transforms of the respective functions. The main contribution of the paper is a proven relationship between the fuzzy transform of the autocorrelation function of a stochastic process and the autocorrelation function of the fuzzy transform of the stochastic process.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Soft Computing

ISSN

1432-7643

e-ISSN

—

Volume of the periodical

25

Issue of the periodical within the volume

12

Country of publishing house

DE - GERMANY

Number of pages

24

Pages from-to

7707-7730

UT code for WoS article

000608667000003

EID of the result in the Scopus database

2-s2.0-85100157572