Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time

Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time

Abstract We consider the problem of sequential inference of latent time-series with innovations correlated in time and observed via nonlinear functions. We accommodate time-varying phenomena with diverse properties by means of a flexible mathematical representation of the data. We characterize stati...

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Bibliographic Details
Main Authors: Iñigo Urteaga, Mónica F. Bugallo, Petar M. Djurić
Format: Article
Language:English
Published: SpringerOpen 2017-12-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Sequential Monte Carlo
Correlated innovations
Latent time-series
State-space models
ARMA
FARIMA
Online Access:http://link.springer.com/article/10.1186/s13634-017-0518-4

Internet

http://link.springer.com/article/10.1186/s13634-017-0518-4

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