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Identification of Autoregressive Moving-Average Parameters of Time Series
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The article was published on 1975-01-01 and is currently open access. It has received 18 citations till now. The article focuses on the topics: Autoregressive integrated moving average & SETAR.read more
Citations
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Journal ArticleDOI
Application of the lattice filter to robust estimation of AR and ARMA models
S. Li,Bradley W. Dickinson +1 more
TL;DR: A (p+q)-stage lattice whitening filter is used to obtained consistent estimates of the AR parameters of an ARMA (p,p) model and a set of MA parameter estimates can also be obtained using this approach with little added computation.
Journal ArticleDOI
A recursive procedure for ARMA modeling
TL;DR: This paper presents a two-part fast recursive algorithm for ARMA modeling that obtains estimates of the p autoregressive coefficients from a set of p extended Yule-Walker equations and derives an exact recursive lattice algorithm for this estimator.
Journal Article
A multifunctional prosthesis control system based on time series identification of EMG signals using microprocessors.
TL;DR: A real-time system for separation among several limb functions, in order to provide multifunctional control of an upper-limb prosthesis for above-elbow amputees, based on identification of voluntary myoelectric signals resulting from isometric contractions of the musculature of the residual limb.
Journal ArticleDOI
Identification of ARMAX models with noisy input and output
TL;DR: A three-step procedure for identifying “ARMAX + noise” processes is proposed that belongs to the errors–in–variables family and allows taking into account the presence of both process disturbances and measurement noise.
Proceedings ArticleDOI
Selection of order and type of time series models estimated from reduced statistics
Piet M.T. Broersen,S. de Waele +1 more
TL;DR: Time series modeling is a parametric solution for spectral analysis and the fit of estimated models to a very long autoregressive model is used for the selection of the type of initial ARMA estimates.
References
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Journal ArticleDOI
Optimal adaptive estimation of sampled stochastic processes
TL;DR: In this article, an adaptive approach to the problem of estimating a sampled, stochastic process described by an initially unknown parameter vector is presented, which is composed of a set of elemental estimators and a corresponding set of weighting coefficients, one pair for each possible value of the parameter vector.
Journal ArticleDOI
Optimal Tracking of Maneuvering Targets
TL;DR: In this article, a binary random variable was introduced in the target state equation for maneuvering target motion. And the optimal estimate was shown to be a weighted combination of two Kalman filter estimates with weights depending on the likelihood ratio for the detection of a maneuver.
Journal ArticleDOI
On the detection of a sudden change in system parameters
TL;DR: By forming an auxiliary sequence of O's and l's as the observations are taken, it is shown, at most, three log-likelihood numbers need to be updated recursively at any stage.