An accurate autoregressive (AR) model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the ...

In this paper a signal modeling technique based upon finite mixture autoregressive probabilistic functions of Markov chains is developed and applied to the problem of speech recognition, particularly speaker-independent recognition of isolated digits. Two types of mixture probability densities are investigated: finite mixtures of Gaussian autoregressive densities (GAM) and nearest-neighbor part...

In this paper, the two-stage procedure is considered for autoregressive parameters estimation in the p-order autoregressive model ( AR(p)). The point estimation and fixed-size confidence ellipsoids construction are investigated which are based on least-squares estimators. Performance criteria are shown including asymptotically risk efficient, asymptotically efficient, and asymptotically consist...

In this paper, we consider a flexible smooth transition autoregressive (STAR) model with multiple regimes and multiple transition variables. This formulation can be interpreted as a time varying linear model where the coefficients are the outputs of a single hidden layer feedforward neural network. This proposal has the major advantage of nesting several nonlinear models, such as, the Self-Exci...

This paper presents a new technique for interpolating missing data in image sequences. A 3D autoregressive (AR) model is employed and a sampling based interpolator is developed in which reconstructed data is generated as a typical realization from the underlying AR process. In this way a perceptually improved result is achieved. A hierarchical gradient-based motion estimator, robust in regions ...

Original scientific paper This paper puts forward a method of fault prognostic based on Autoregressive Support Vector Regression Method (AR-LSSVR) for electrolytic capacitor. Because the electrolytic capacitor is low in cost and large in volume, it is widely used in power electronic circuits. Firstly it introduces the basic model and the fault prognostic algorithm of the AR, LSSVM and AR-LSSVR....

Glossary AR(1): Autoregressive model of order one. The present state of a system can be described as a linear function of the state at the previous time, plus timeindependent noise. data assimilation: Method of optimally combining irregularly spaced observations with dynamical constraints to produce dynamically consistent fields on regular grids. CCA: Canonical Correlation Analysis EOF: Empiric...

This brief addresses a new method for autoregressive (AR) parameter estimation from colored noise-corrupted observations using a damped sinusoidal model for autocorrelation function of the noise-free signal. The damped sinusoidal model parameters are first estimated using a least-squares based method from the given noisy observations. The AR parameters are then directly obtained from the damped...

|EEG signals are modeled using single-channel and multi-channel autoregressive (AR) techniques. The co-eecients of these models are used to classify EEG data into one of two classes corresponding to the mental task the subjects are performing. A neural network is trained to perform the classiication. When applying a trained network to test data, we nd that the multivariate AR representation per...

This paper evaluates the performance of prediction intervals generated from alternative time series models, in the context of tourism forecasting. The forecasting methods considered include the autoregressive (AR) model, the AR model using the bias-corrected bootstrap, seasonal ARIMA models, innovations state-space models for exponential smoothing, and Harvey’s structural time series models. We...