Time Series Identification Using Monte Carlo Method

Teresa Glowka

doi:10.1007/978-3-031-35170-9_6

Time Series Identification Using Monte Carlo Method

Teresa Glowka

Department of Measurements and Control Systems (RAU/RAU2)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper focuses on time series (namely autoregressive, moving-average, and autoregressive-moving-average) models identification using one of the most universal Monte Carlo method, the Metropolis-Hasting algorithm. Theoretical formulation of a problem as well as some identification results are given.

Original language	English
Title of host publication	Advanced, Contemporary Control - Proceedings of the 21st Polish Control Conference, 2023, Volume 1
Editors	Marek Pawelczyk, Dariusz Bismor, Szymon Ogonowski, Janusz Kacprzyk
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	59-68
Number of pages	10
ISBN (Print)	9783031351693
DOIs	https://doi.org/10.1007/978-3-031-35170-9_6
Publication status	Published - 2023
Event	21st Polish Control Conference, PCC 2023 - Gliwice, Poland Duration: 26 Jun 2023 → 29 Jun 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	708
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	21st Polish Control Conference, PCC 2023
Country/Territory	Poland
City	Gliwice
Period	26/06/23 → 29/06/23

Keywords

Metropolis-Hasting algorithm
Monte Carlo methods
statistical signal processing
time series identification

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-031-35170-9_6

Cite this

Glowka, T. (2023). Time Series Identification Using Monte Carlo Method. In M. Pawelczyk, D. Bismor, S. Ogonowski, & J. Kacprzyk (Eds.), Advanced, Contemporary Control - Proceedings of the 21st Polish Control Conference, 2023, Volume 1 (pp. 59-68). (Lecture Notes in Networks and Systems; Vol. 708). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-35170-9_6

@inproceedings{b19eb14264194e0aa0fee694e07e64c3,

title = "Time Series Identification Using Monte Carlo Method",

abstract = "This paper focuses on time series (namely autoregressive, moving-average, and autoregressive-moving-average) models identification using one of the most universal Monte Carlo method, the Metropolis-Hasting algorithm. Theoretical formulation of a problem as well as some identification results are given.",

keywords = "Metropolis-Hasting algorithm, Monte Carlo methods, statistical signal processing, time series identification",

author = "Teresa Glowka",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; 21st Polish Control Conference, PCC 2023 ; Conference date: 26-06-2023 Through 29-06-2023",

year = "2023",

doi = "10.1007/978-3-031-35170-9\_6",

language = "English",

isbn = "9783031351693",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "59--68",

editor = "Marek Pawelczyk and Dariusz Bismor and Szymon Ogonowski and Janusz Kacprzyk",

booktitle = "Advanced, Contemporary Control - Proceedings of the 21st Polish Control Conference, 2023, Volume 1",

address = "Germany",

}

Glowka, T 2023, Time Series Identification Using Monte Carlo Method. in M Pawelczyk, D Bismor, S Ogonowski & J Kacprzyk (eds), Advanced, Contemporary Control - Proceedings of the 21st Polish Control Conference, 2023, Volume 1. Lecture Notes in Networks and Systems, vol. 708, Springer Science and Business Media Deutschland GmbH, pp. 59-68, 21st Polish Control Conference, PCC 2023, Gliwice, Poland, 26/06/23. https://doi.org/10.1007/978-3-031-35170-9_6

Time Series Identification Using Monte Carlo Method. / Glowka, Teresa.
Advanced, Contemporary Control - Proceedings of the 21st Polish Control Conference, 2023, Volume 1. ed. / Marek Pawelczyk; Dariusz Bismor; Szymon Ogonowski; Janusz Kacprzyk. Springer Science and Business Media Deutschland GmbH, 2023. p. 59-68 (Lecture Notes in Networks and Systems; Vol. 708).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Time Series Identification Using Monte Carlo Method

AU - Glowka, Teresa

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

PY - 2023

Y1 - 2023

N2 - This paper focuses on time series (namely autoregressive, moving-average, and autoregressive-moving-average) models identification using one of the most universal Monte Carlo method, the Metropolis-Hasting algorithm. Theoretical formulation of a problem as well as some identification results are given.

AB - This paper focuses on time series (namely autoregressive, moving-average, and autoregressive-moving-average) models identification using one of the most universal Monte Carlo method, the Metropolis-Hasting algorithm. Theoretical formulation of a problem as well as some identification results are given.

KW - Metropolis-Hasting algorithm

KW - Monte Carlo methods

KW - statistical signal processing

KW - time series identification

UR - https://www.scopus.com/pages/publications/85175694651

U2 - 10.1007/978-3-031-35170-9_6

DO - 10.1007/978-3-031-35170-9_6

M3 - Conference contribution

AN - SCOPUS:85175694651

SN - 9783031351693

T3 - Lecture Notes in Networks and Systems

SP - 59

EP - 68

BT - Advanced, Contemporary Control - Proceedings of the 21st Polish Control Conference, 2023, Volume 1

A2 - Pawelczyk, Marek

A2 - Bismor, Dariusz

A2 - Ogonowski, Szymon

A2 - Kacprzyk, Janusz

PB - Springer Science and Business Media Deutschland GmbH

T2 - 21st Polish Control Conference, PCC 2023

Y2 - 26 June 2023 through 29 June 2023

ER -

Time Series Identification Using Monte Carlo Method

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this