Time-dependent ARMA modeling of nonstationary signals

By Y. Grenier. 1983

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Wavenet Based Low Rate Speech Coding (2017, methodology): “Reﬁned generative models such as ARMA, e.g., [9], and kernel density estimation (KDE) HMM, e.g., [10], can also be used.”
Inference of time-varying regression models (2012, background): “A traditional approach is to project the coefficient function onto a basis of temporal functions, and estimates the basis coefficients; see, for example, [25, 43, 47].”
Locally Stationary Processes (2011, background): “Furthermore, there have been several papers on inference for processes with time varying parameters – mainly within the engineering literature (cf. Grenier (1983), Kayhan et.al.”
Output-only identification and dynamic analysis of time-varying mechanical structures under random excitation: A comparative assessment of parametric methods (2010, methodology): “Two such methods, the two stage least squares (2SLS) method [2, 36] and the polynomial-algebraic (P-A) method [30] are outlined in Fig.”
On recursive estimation for time varying autoregressive processes (2005, methodology): “ere Xk,n :=[Xk,nXk−1,n ···Xk−d+1,n] T , θk,n :=θ(k/n)=[θ 1(k/n)θ 2(k/n)···θd(k/n)]T and σk,n =σ(k/n). TVAR models have been used for modeling data whose spectral content varies along time (see, e.g., [12, 15, 22] for early references). TVAR models are also closely related to the general class of loc”