Prony estimation of AR parameters of an ARMA time series
نویسندگان
چکیده
منابع مشابه
Spectral Estimation of Stationary Time Series: Recent Developments
Spectral analysis considers the problem of determining (the art of recovering) the spectral content (i.e., the distribution of power over frequency) of a stationary time series from a finite set of measurements, by means of either nonparametric or parametric techniques. This paper introduces the spectral analysis problem, motivates the definition of power spectral density functions, and reviews...
متن کاملEvolving Time Series Forecasting ARMA Models
Time Series Forecasting (TSF) allows the modeling of complex systems as “black-boxes”, being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popul...
متن کاملARMA Time-Series Modeling with Graphical Models
We express the classic ARMA time-series model as a directed graphical model. In doing so, we find that the deterministic relationships in the model make it effectively impossible to use the EM algorithm for learning model parameters. To remedy this problem, we replace the deterministic relationships with Gaussian distributions having a small variance, yielding the stochastic ARMA (σARMA) model....
متن کاملEstimation of AR Parameters in the Presence of Additive Contamination in the Infinite Variance Case
If we try to estimate the parameters of the AR process {Xn} using the observed process {Xn+Zn} then these estimates will be badly biased and not consistent but we can minimize the damage using a robust estimation procedure such as GM-estimation. The question is does additive contamination affect estimates of “core” parameters in the infinite variance case to the same extent that it does in the ...
متن کاملEnforcing solvability of a nonlinear matrix equation and estimation of multivariate ARMA time series
The matrix equation X +AX−1AT = B, arising in parameter estimation of certain time series models, is solvable only for certain values of the matrices A,B. We present a numerical method to modify A,B in order to make the matrix equation solvable. Since solvability depends on the location of the eigenvalues of the palindromic matrix polynomial λA+ λB+A , our method works by moving those eigenvalu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mechanical Systems and Signal Processing
سال: 1989
ISSN: 0888-3270
DOI: 10.1016/0888-3270(89)90017-4