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 -