A Time-Varying Autoregressive Model for Characterizing Nonstationary Processes

被引：8

作者：

de Souza, Douglas Baptista ^{[1
]}

Kuhn, Eduardo Vinicius ^{[2
]}

Seara, Rui ^{[3
]}

机构：

[1] GE Renewable Energy, Data Analyt Team, Sao Paulo, SP, Brazil

[2] Univ Tecnol Fed Parana, Dept Elect Engn, LAPSE Elect & Signal Proc Lab, BR-85902490 Toledo, Brazil

[3] Univ Fed Santa Catarina, Dept Elect & Elect Engn, LINSE Circuits & Signal Proc Lab, BR-88040900 Florianopolis, SC, Brazil

来源：

IEEE SIGNAL PROCESSING LETTERS | 2019年 / 26卷 / 01期

关键词：

Nonstationary processes; stochastic analysis; time-varying autoregressive (TVAR) model; ALGORITHM;

D O I：

10.1109/LSP.2018.2880086

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：134 / 138

页数：5

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