scholarly journals Bayesian Multiperiod Forecasting for Arma Model under Jeffrey's Prior

2015 ◽  
Vol 3 (3) ◽  
pp. 65-70
Author(s):  
Zul Amry ◽  
Adam Baharum
Keyword(s):  
Arma Model ◽  
Jeffrey's Prior

scholarly journals Identification of Linear Systems Using MA and ARMA Model in Dioid

1998 ◽  
Vol 31 (18) ◽  
pp. 561-567
Author(s):  
F. Gallot ◽  
J.L. Boimond ◽  
L. Hardouin
Keyword(s):  
Linear Systems ◽  
Arma Model

Incipient fault diagnosis of limit switch based on a ARMA model

Measurement ◽  
2019 ◽  
Vol 135 ◽  
pp. 473-480 ◽  
Author(s):  
Ling Wang ◽  
Jin Pan ◽  
Yanfeng Gao ◽  
Bingrui Wang ◽  
Kaixing Hong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Arma Model ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis ◽  
Limit Switch

scholarly journals An Extended Time Series Algorithm for Modal Identification from Nonstationary Ambient Response Data Only

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chang-Sheng Lin ◽  
Dar-Yun Chiang ◽  
Tse-Chuan Tseng
Keyword(s):  
Time Series ◽  
Arma Model ◽  
Identification Problem ◽  
Nonstationary Time Series ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Time Varying ◽  
Varying Parameters ◽  
Response Data ◽  
Time Varying Parameters

Modal Identification is considered from response data of structural systems under nonstationary ambient vibration. The conventional autoregressive moving average (ARMA) algorithm is applicable to perform modal identification, however, only for stationary-process vibration. The ergodicity postulate which has been conventionally employed for stationary processes is no longer valid in the case of nonstationary analysis. The objective of this paper is therefore to develop modal-identification techniques based on the nonstationary time series for linear systems subjected to nonstationary ambient excitation. Nonstationary ARMA model with time-varying parameters is considered because of its capability of resolving general nonstationary problems. The parameters of moving averaging (MA) model in the nonstationary time-series algorithm are treated as functions of time and may be represented by a linear combination of base functions and therefore can be used to solve the identification problem of time-varying parameters. Numerical simulations confirm the validity of the proposed modal-identification method from nonstationary ambient response data.

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