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A Time-Varying Autoregressive Model for Characterizing Nonstationary Processes | IEEE Journals & Magazine | IEEE Xplore

A Time-Varying Autoregressive Model for Characterizing Nonstationary Processes


Abstract:

This letter presents a time-varying autoregressive (TVAR) model aiming to characterize nonstationary behaviors often observed in real-world processes, which cannot be pro...Show More

Abstract:

This letter presents a time-varying autoregressive (TVAR) model aiming to characterize nonstationary behaviors often observed in real-world processes, which cannot be properly described by autoregressive processes such as first-order Markov and random-walk models. Specifically, general model expressions for the mean vector and covariance matrix of the TVAR model are firstly derived. Then, such expressions are used to guide the design of two special setups for the TVAR model. The capability of the developed model to reproduce important nonstationary behaviors is verified mathematically and through simulations.
Published in: IEEE Signal Processing Letters ( Volume: 26, Issue: 1, January 2019)
Page(s): 134 - 138
Date of Publication: 07 November 2018

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